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Janizek JD, Dincer AB, Celik S, Chen H, Chen W, Naxerova K, Lee SI. Uncovering expression signatures of synergistic drug responses via ensembles of explainable machine-learning models. Nat Biomed Eng 2023; 7:811-829. [PMID: 37127711 PMCID: PMC11149694 DOI: 10.1038/s41551-023-01034-0] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/12/2021] [Accepted: 04/01/2023] [Indexed: 05/03/2023]
Abstract
Machine learning may aid the choice of optimal combinations of anticancer drugs by explaining the molecular basis of their synergy. By combining accurate models with interpretable insights, explainable machine learning promises to accelerate data-driven cancer pharmacology. However, owing to the highly correlated and high-dimensional nature of transcriptomic data, naively applying current explainable machine-learning strategies to large transcriptomic datasets leads to suboptimal outcomes. Here by using feature attribution methods, we show that the quality of the explanations can be increased by leveraging ensembles of explainable machine-learning models. We applied the approach to a dataset of 133 combinations of 46 anticancer drugs tested in ex vivo tumour samples from 285 patients with acute myeloid leukaemia and uncovered a haematopoietic-differentiation signature underlying drug combinations with therapeutic synergy. Ensembles of machine-learning models trained to predict drug combination synergies on the basis of gene-expression data may improve the feature attribution quality of complex machine-learning models.
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Affiliation(s)
- Joseph D Janizek
- Paul G. Allen School of Computer Science and Engineering, University of Washington, Seattle, WA, USA
- Medical Scientist Training Program, University of Washington, Seattle, WA, USA
| | - Ayse B Dincer
- Paul G. Allen School of Computer Science and Engineering, University of Washington, Seattle, WA, USA
| | - Safiye Celik
- Recursion Pharmaceuticals, Salt Lake City, UT, USA
| | - Hugh Chen
- Paul G. Allen School of Computer Science and Engineering, University of Washington, Seattle, WA, USA
| | - William Chen
- Paul G. Allen School of Computer Science and Engineering, University of Washington, Seattle, WA, USA
| | - Kamila Naxerova
- Center for Systems Biology, Massachusetts General Hospital and Harvard Medical School, Boston, MA, USA.
- Department of Radiology, Massachusetts General Hospital and Harvard Medical School, Boston, MA, USA.
| | - Su-In Lee
- Paul G. Allen School of Computer Science and Engineering, University of Washington, Seattle, WA, USA.
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2
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Sarkar C, Das B, Rawat VS, Wahlang JB, Nongpiur A, Tiewsoh I, Lyngdoh NM, Das D, Bidarolli M, Sony HT. Artificial Intelligence and Machine Learning Technology Driven Modern Drug Discovery and Development. Int J Mol Sci 2023; 24:ijms24032026. [PMID: 36768346 PMCID: PMC9916967 DOI: 10.3390/ijms24032026] [Citation(s) in RCA: 14] [Impact Index Per Article: 14.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/29/2022] [Revised: 12/27/2022] [Accepted: 12/28/2022] [Indexed: 01/22/2023] Open
Abstract
The discovery and advances of medicines may be considered as the ultimate relevant translational science effort that adds to human invulnerability and happiness. But advancing a fresh medication is a quite convoluted, costly, and protracted operation, normally costing USD ~2.6 billion and consuming a mean time span of 12 years. Methods to cut back expenditure and hasten new drug discovery have prompted an arduous and compelling brainstorming exercise in the pharmaceutical industry. The engagement of Artificial Intelligence (AI), including the deep-learning (DL) component in particular, has been facilitated by the employment of classified big data, in concert with strikingly reinforced computing prowess and cloud storage, across all fields. AI has energized computer-facilitated drug discovery. An unrestricted espousing of machine learning (ML), especially DL, in many scientific specialties, and the technological refinements in computing hardware and software, in concert with various aspects of the problem, sustain this progress. ML algorithms have been extensively engaged for computer-facilitated drug discovery. DL methods, such as artificial neural networks (ANNs) comprising multiple buried processing layers, have of late seen a resurgence due to their capability to power automatic attribute elicitations from the input data, coupled with their ability to obtain nonlinear input-output pertinencies. Such features of DL methods augment classical ML techniques which bank on human-contrived molecular descriptors. A major part of the early reluctance concerning utility of AI in pharmaceutical discovery has begun to melt, thereby advancing medicinal chemistry. AI, along with modern experimental technical knowledge, is anticipated to invigorate the quest for new and improved pharmaceuticals in an expeditious, economical, and increasingly compelling manner. DL-facilitated methods have just initiated kickstarting for some integral issues in drug discovery. Many technological advances, such as "message-passing paradigms", "spatial-symmetry-preserving networks", "hybrid de novo design", and other ingenious ML exemplars, will definitely come to be pervasively widespread and help dissect many of the biggest, and most intriguing inquiries. Open data allocation and model augmentation will exert a decisive hold during the progress of drug discovery employing AI. This review will address the impending utilizations of AI to refine and bolster the drug discovery operation.
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Affiliation(s)
- Chayna Sarkar
- Department of Pharmacology, North Eastern Indira Gandhi Regional Institute of Health and Medical Sciences (NEIGRIHMS), Mawdiangdiang, Shillong 793018, Meghalaya, India
| | - Biswadeep Das
- Department of Pharmacology, All India Institute of Medical Sciences (AIIMS), Virbhadra Road, Rishikesh 249203, Uttarakhand, India
- Correspondence: ; Tel./Fax: +91-135-708-856-0009
| | - Vikram Singh Rawat
- Department of Psychiatry, All India Institute of Medical Sciences (AIIMS), Virbhadra Road, Rishikesh 249203, Uttarakhand, India
| | - Julie Birdie Wahlang
- Department of Pharmacology, North Eastern Indira Gandhi Regional Institute of Health and Medical Sciences (NEIGRIHMS), Mawdiangdiang, Shillong 793018, Meghalaya, India
| | - Arvind Nongpiur
- Department of Psychiatry, North Eastern Indira Gandhi Regional Institute of Health and Medical Sciences (NEIGRIHMS), Mawdiangdiang, Shillong 793018, Meghalaya, India
| | - Iadarilang Tiewsoh
- Department of Medicine, North Eastern Indira Gandhi Regional Institute of Health and Medical Sciences (NEIGRIHMS), Mawdiangdiang, Shillong 793018, Meghalaya, India
| | - Nari M. Lyngdoh
- Department of Anesthesiology, North Eastern Indira Gandhi Regional Institute of Health and Medical Sciences (NEIGRIHMS), Mawdiangdiang, Shillong 793018, Meghalaya, India
| | - Debasmita Das
- Department of Computer Science and Engineering, Vellore Institute of Technology, Vellore Campus, Tiruvalam Road, Katpadi, Vellore 632014, Tamil Nadu, India
| | - Manjunath Bidarolli
- Department of Pharmacology, All India Institute of Medical Sciences (AIIMS), Virbhadra Road, Rishikesh 249203, Uttarakhand, India
| | - Hannah Theresa Sony
- Department of Pharmacology, All India Institute of Medical Sciences (AIIMS), Virbhadra Road, Rishikesh 249203, Uttarakhand, India
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Nafshi R, Lezon TR. Predicting the Effects of Drug Combinations Using Probabilistic Matrix Factorization. FRONTIERS IN BIOINFORMATICS 2021; 1:708815. [PMID: 36303743 PMCID: PMC9581062 DOI: 10.3389/fbinf.2021.708815] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/12/2021] [Accepted: 07/30/2021] [Indexed: 12/12/2022] Open
Abstract
Drug development is costly and time-consuming, and developing novel practical strategies for creating more effective treatments is imperative. One possible solution is to prescribe drugs in combination. Synergistic drug combinations could allow lower doses of each constituent drug, reducing adverse reactions and drug resistance. However, it is not feasible to sufficiently test every combination of drugs for a given illness to determine promising synergistic combinations. Since there is a finite amount of time and resources available for finding synergistic combinations, a model that can identify synergistic combinations from a limited subset of all available combinations could accelerate development of therapeutics. By applying recommender algorithms, such as the low-rank matrix completion algorithm Probabilistic Matrix Factorization (PMF), it may be possible to identify synergistic combinations from partial information of the drug interactions. Here, we use PMF to predict the efficacy of two-drug combinations using the NCI ALMANAC, a robust collection of pairwise drug combinations of 104 FDA-approved anticancer drugs against 60 common cancer cell lines. We find that PMF is able predict drug combination efficacy with high accuracy from a limited set of combinations and is robust to changes in the individual training data. Moreover, we propose a new PMF-guided experimental design to detect all synergistic combinations without testing every combination.
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4
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Pires JG, da Silva GF, Weyssow T, Conforte AJ, Pagnoncelli D, da Silva FAB, Carels N. Galaxy and MEAN Stack to Create a User-Friendly Workflow for the Rational Optimization of Cancer Chemotherapy. Front Genet 2021; 12:624259. [PMID: 33679888 PMCID: PMC7935533 DOI: 10.3389/fgene.2021.624259] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/31/2020] [Accepted: 01/22/2021] [Indexed: 12/24/2022] Open
Abstract
One aspect of personalized medicine is aiming at identifying specific targets for therapy considering the gene expression profile of each patient individually. The real-world implementation of this approach is better achieved by user-friendly bioinformatics systems for healthcare professionals. In this report, we present an online platform that endows users with an interface designed using MEAN stack supported by a Galaxy pipeline. This pipeline targets connection hubs in the subnetworks formed by the interactions between the proteins of genes that are up-regulated in tumors. This strategy has been proved to be suitable for the inhibition of tumor growth and metastasis in vitro. Therefore, Perl and Python scripts were enclosed in Galaxy for translating RNA-seq data into protein targets suitable for the chemotherapy of solid tumors. Consequently, we validated the process of target diagnosis by (i) reference to subnetwork entropy, (ii) the critical value of density probability of differential gene expression, and (iii) the inhibition of the most relevant targets according to TCGA and GDC data. Finally, the most relevant targets identified by the pipeline are stored in MongoDB and can be accessed through the aforementioned internet portal designed to be compatible with mobile or small devices through Angular libraries.
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Affiliation(s)
- Jorge Guerra Pires
- Plataforma de Modelagem de Sistemas Biológicos, Center for Technology Development in Health (CDTS), Oswaldo Cruz Foundation (FIOCRUZ), Rio de Janeiro, Brazil
| | - Gilberto Ferreira da Silva
- Plataforma de Modelagem de Sistemas Biológicos, Center for Technology Development in Health (CDTS), Oswaldo Cruz Foundation (FIOCRUZ), Rio de Janeiro, Brazil
| | - Thomas Weyssow
- Informatic Department, Free University of Brussels (ULB), Brussels, Belgium
| | - Alessandra Jordano Conforte
- Plataforma de Modelagem de Sistemas Biológicos, Center for Technology Development in Health (CDTS), Oswaldo Cruz Foundation (FIOCRUZ), Rio de Janeiro, Brazil.,Laboratório de Modelagem Computacional de Sistemas Biológicos, Scientific Computing Program, FIOCRUZ, Rio de Janeiro, Brazil
| | | | - Fabricio Alves Barbosa da Silva
- Laboratório de Modelagem Computacional de Sistemas Biológicos, Scientific Computing Program, FIOCRUZ, Rio de Janeiro, Brazil
| | - Nicolas Carels
- Plataforma de Modelagem de Sistemas Biológicos, Center for Technology Development in Health (CDTS), Oswaldo Cruz Foundation (FIOCRUZ), Rio de Janeiro, Brazil
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5
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Paul D, Sanap G, Shenoy S, Kalyane D, Kalia K, Tekade RK. Artificial intelligence in drug discovery and development. Drug Discov Today 2021; 26:80-93. [PMID: 33099022 PMCID: PMC7577280 DOI: 10.1016/j.drudis.2020.10.010] [Citation(s) in RCA: 314] [Impact Index Per Article: 104.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/21/2020] [Revised: 09/03/2020] [Accepted: 10/13/2020] [Indexed: 02/07/2023]
Abstract
Artificial intelligence-integrated drug discovery and development has accelerated the growth of the pharmaceutical sector, leading to a revolutionary change in the pharma industry. Here, we discuss areas of integration, tools, and techniques utilized in enforcing AI, ongoing challenges, and ways to overcome them.
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Affiliation(s)
- Debleena Paul
- National Institute of Pharmaceutical Education and Research-Ahmedabad (NIPER-A), An Institute of National Importance, Government of India, Department of Pharmaceuticals, Ministry of Chemicals and Fertilizers, Palaj, Opp. Air Force Station, Gandhinagar, 382355, Gujarat, India
| | - Gaurav Sanap
- National Institute of Pharmaceutical Education and Research-Ahmedabad (NIPER-A), An Institute of National Importance, Government of India, Department of Pharmaceuticals, Ministry of Chemicals and Fertilizers, Palaj, Opp. Air Force Station, Gandhinagar, 382355, Gujarat, India
| | - Snehal Shenoy
- National Institute of Pharmaceutical Education and Research-Ahmedabad (NIPER-A), An Institute of National Importance, Government of India, Department of Pharmaceuticals, Ministry of Chemicals and Fertilizers, Palaj, Opp. Air Force Station, Gandhinagar, 382355, Gujarat, India
| | - Dnyaneshwar Kalyane
- National Institute of Pharmaceutical Education and Research-Ahmedabad (NIPER-A), An Institute of National Importance, Government of India, Department of Pharmaceuticals, Ministry of Chemicals and Fertilizers, Palaj, Opp. Air Force Station, Gandhinagar, 382355, Gujarat, India
| | - Kiran Kalia
- National Institute of Pharmaceutical Education and Research-Ahmedabad (NIPER-A), An Institute of National Importance, Government of India, Department of Pharmaceuticals, Ministry of Chemicals and Fertilizers, Palaj, Opp. Air Force Station, Gandhinagar, 382355, Gujarat, India
| | - Rakesh K Tekade
- National Institute of Pharmaceutical Education and Research-Ahmedabad (NIPER-A), An Institute of National Importance, Government of India, Department of Pharmaceuticals, Ministry of Chemicals and Fertilizers, Palaj, Opp. Air Force Station, Gandhinagar, 382355, Gujarat, India.
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Abstract
Currently, the development of medicines for complex diseases requires the development of combination drug therapies. It is necessary because in many cases, one drug cannot target all necessary points of intervention. For example, in cancer therapy, a physician often meets a patient having a genomic profile including more than five molecular aberrations. Drug combination therapy has been an area of interest for a while, for example the classical work of Loewe devoted to the synergism of drugs was published in 1928-and it is still used in calculations for optimal drug combinations. More recently, over the past several years, there has been an explosion in the available information related to the properties of drugs and the biomedical parameters of patients. For the drugs, hundreds of 2D and 3D molecular descriptors for medicines are now available, while for patients, large data sets related to genetic/proteomic and metabolomics profiles of the patients are now available, as well as the more traditional data relating to the histology, history of treatments, pretreatment state of the organism, etc. Moreover, during disease progression, the genetic profile can change. Thus, the ability to optimize drug combinations for each patient is rapidly moving beyond the comprehension and capabilities of an individual physician. This is the reason, that biomedical informatics methods have been developed and one of the more promising directions in this field is the application of artificial intelligence (AI). In this review, we discuss several AI methods that have been successfully implemented in several instances of combination drug therapy from HIV, hypertension, infectious diseases to cancer. The data clearly show that the combination of rule-based expert systems with machine learning algorithms may be promising direction in this field.
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7
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Kim MM, Audet J. On-demand serum-free media formulations for human hematopoietic cell expansion using a high dimensional search algorithm. Commun Biol 2019; 2:48. [PMID: 30729186 PMCID: PMC6358607 DOI: 10.1038/s42003-019-0296-7] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/14/2018] [Accepted: 01/09/2019] [Indexed: 12/13/2022] Open
Abstract
Substitution of serum and other clinically incompatible reagents is requisite for controlling product quality in a therapeutic cell manufacturing process. However, substitution with chemically defined compounds creates a complex, large-scale optimization problem due to the large number of possible factors and dose levels, making conventional process optimization methods ineffective. We present a framework for high-dimensional optimization of serum-free formulations for the expansion of human hematopoietic cells. Our model-free approach utilizes evolutionary computing principles to drive an experiment-based feedback control platform. We validate this method by optimizing serum-free formulations for first, TF-1 cells and second, primary T-cells. For each cell type, we successfully identify a set of serum-free formulations that support cell expansions similar to the serum-containing conditions commonly used to culture these cells, by experimentally testing less than 1 × 10-5 % of the total search space. We also demonstrate how this iterative search process can provide insights into factor interactions that contribute to supporting cell expansion.
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Affiliation(s)
- Michelle M. Kim
- Institute of Biomaterials and Biomedical Engineering, University of Toronto, 164 College St, Toronto, ON M5S 3G9 Canada
| | - Julie Audet
- Institute of Biomaterials and Biomedical Engineering, University of Toronto, 164 College St, Toronto, ON M5S 3G9 Canada
- Department of Chemical Engineering and Applied Chemistry, University of Toronto, 200 College St, Toronto, ON M5S 3E5 Canada
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8
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Katzir I, Cokol M, Aldridge BB, Alon U. Prediction of ultra-high-order antibiotic combinations based on pairwise interactions. PLoS Comput Biol 2019; 15:e1006774. [PMID: 30699106 PMCID: PMC6370231 DOI: 10.1371/journal.pcbi.1006774] [Citation(s) in RCA: 42] [Impact Index Per Article: 8.4] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/28/2018] [Revised: 02/11/2019] [Accepted: 01/10/2019] [Indexed: 11/19/2022] Open
Abstract
Drug combinations are a promising approach to achieve high efficacy at low doses and to overcome resistance. Drug combinations are especially useful when drugs cannot achieve effectiveness at tolerable doses, as occurs in cancer and tuberculosis (TB). However, discovery of effective drug combinations faces the challenge of combinatorial explosion, in which the number of possible combinations increases exponentially with the number of drugs and doses. A recent advance, called the dose model, uses a mathematical formula to overcome combinatorial explosion by reducing the problem to a feasible quadratic one: using data on drug pairs at a few doses, the dose model accurately predicts the effect of combinations of three and four drugs at all doses. The dose model has not yet been tested on higher-order combinations beyond four drugs. To address this, we measured the effect of combinations of up to ten antibiotics on E. coli growth, and of up to five tuberculosis (TB) drugs on the growth of M. tuberculosis. We find that the dose model accurately predicts the effect of these higher-order combinations, including cases of strong synergy and antagonism. This study supports the view that the interactions between drug pairs carries key information that largely determines higher-order interactions. Therefore, systematic study of pairwise drug interactions is a compelling strategy to prioritize drug regimens in high-dimensional spaces.
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Affiliation(s)
- Itay Katzir
- Department of Molecular Cell Biology, Weizmann Institute of Science, Rehovot, Israel
| | - Murat Cokol
- Axcella Health, Cambridge, MA
- Laboratory of Systems Pharmacology, Harvard Medical School, Boston MA
| | - Bree B. Aldridge
- Laboratory of Systems Pharmacology, Harvard Medical School, Boston MA
- Department of Molecular Biology and Microbiology, Tufts University School of Medicine, Boston, MA
- Department of Biomedical Engineering, Tufts University School of Engineering, Medford, MA
- * E-mail: (BBA); (UA)
| | - Uri Alon
- Department of Molecular Cell Biology, Weizmann Institute of Science, Rehovot, Israel
- * E-mail: (BBA); (UA)
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9
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Yin Z, Deng Z, Zhao W, Cao Z. Searching Synergistic Dose Combinations for Anticancer Drugs. Front Pharmacol 2018; 9:535. [PMID: 29872399 PMCID: PMC5972206 DOI: 10.3389/fphar.2018.00535] [Citation(s) in RCA: 19] [Impact Index Per Article: 3.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/06/2018] [Accepted: 05/03/2018] [Indexed: 01/01/2023] Open
Abstract
Recent development has enabled synergistic drugs in treating a wide range of cancers. Being highly context-dependent, however, identification of successful ones often requires screening of combinational dose on different testing platforms in order to gain the best anticancer effects. To facilitate the development of effective computational models, we reviewed the latest strategy in searching optimal dose combination from three perspectives: (1) mainly experimental-based approach; (2) Computational-guided experimental approach; and (3) mainly computational-based approach. In addition to the introduction of each strategy, critical discussion of their advantages and disadvantages were also included, with a strong focus on the current applications and future improvements.
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Affiliation(s)
- Zuojing Yin
- Shanghai Tenth People's Hospital, School of Life Sciences and Technology, Tongji University, Shanghai, China
| | - Zeliang Deng
- Shanghai Tenth People's Hospital, School of Life Sciences and Technology, Tongji University, Shanghai, China
| | - Wenyan Zhao
- Shanghai Tenth People's Hospital, School of Life Sciences and Technology, Tongji University, Shanghai, China
| | - Zhiwei Cao
- Shanghai Tenth People's Hospital, School of Life Sciences and Technology, Tongji University, Shanghai, China
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Zimmer A, Tendler A, Katzir I, Mayo A, Alon U. Prediction of drug cocktail effects when the number of measurements is limited. PLoS Biol 2017; 15:e2002518. [PMID: 29073201 PMCID: PMC5675459 DOI: 10.1371/journal.pbio.2002518] [Citation(s) in RCA: 21] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/08/2017] [Revised: 11/07/2017] [Accepted: 10/10/2017] [Indexed: 12/15/2022] Open
Abstract
Cocktails of drugs can be more effective than single drugs, because they can potentially work at lower doses and avoid resistance. However, it is impossible to test all drug cocktails drawn from a large set of drugs because of the huge number of combinations. To overcome this combinatorial explosion problem, one can sample a relatively small number of combinations and use a model to predict the rest. Recently, Zimmer and Katzir et al. presented a model that accurately predicted the effects of cocktails at all doses based on measuring pairs of drugs. This model requires measuring each pair at several different doses and uses interpolation to reduce experimental noise. However, often, it is not possible to measure each pair at multiple doses (for example, in scarce patient-derived tumor material or in large screens). Here, we ask whether measurements at only a single dose can also predict high-order drug cocktails. To address this, we present a fully factorial experimental dataset on all drug cocktails built of 6 chemotherapy drugs on 2 cancer cell lines. We develop a formula that uses only pair measurements at a single dose to predict much of the variation up to 6-drug cocktails in the present data, outperforming commonly used Bliss independence and regression approaches. This model, called the pairs model, is an extension of the Bliss independence model to pairs: For M drugs, it equals the product of all pair effects to the power 1/(M−1). The pairs model also shows good agreement with previously published data on antibiotic triplets and quadruplets. The present model can only predict combinations at the same doses in which the pairs were measured and is not able to predict effects at other doses. This study indicates that pair-based approaches might be able to usefully predict and prioritize high-order combinations, even in large screens or when material for testing is limited. Drug cocktails are a promising strategy for diseases such as cancer and infections, because cocktails can be more effective than individual drugs and can overcome problems of drug resistance. However, finding the best cocktail comprising a given set of drugs is challenging because the number of experiments needed is huge and grows exponentially with the number of drugs. This problem is exacerbated when the experiments are expensive and the material for testing is rare. Here, we present a way to address this challenge using a mathematical formula, called the pairs model, that requires relatively few experimental tests in order to estimate the effects of cocktails and to predict which cocktail is most effective. The formula does well on experimental data generated in this study using combinations of between 3 and 6 anticancer drugs, as well as on existing data that use combinations of antibiotics.
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Affiliation(s)
- Anat Zimmer
- Department of Molecular Cell Biology, Weizmann Institute of Science, Rehovot, Israel
| | - Avichai Tendler
- Department of Molecular Cell Biology, Weizmann Institute of Science, Rehovot, Israel
| | - Itay Katzir
- Department of Molecular Cell Biology, Weizmann Institute of Science, Rehovot, Israel
| | - Avi Mayo
- Department of Molecular Cell Biology, Weizmann Institute of Science, Rehovot, Israel
| | - Uri Alon
- Department of Molecular Cell Biology, Weizmann Institute of Science, Rehovot, Israel
- * E-mail:
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11
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Matlock K, Berlow N, Keller C, Pal R. Combination therapy design for maximizing sensitivity and minimizing toxicity. BMC Bioinformatics 2017; 18:116. [PMID: 28361667 PMCID: PMC5374708 DOI: 10.1186/s12859-017-1523-1] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/22/2023] Open
Abstract
Background Design of personalized targeted therapies involve modeling of patient sensitivity to various drugs and drug combinations. Majority of studies evaluate the sensitivity of tumor cells to targeted drugs without modeling the effect of the drugs on normal cells. In this article, we consider the individual modeling of drug responses to tumor and normal cells and utilize them to design targeted combination therapies that maximize sensitivity over tumor cells and minimize toxicity over normal cells. Results The problem is formulated as maximizing sensitivity over tumor cell models while maintaining sensitivity below a threshold over normal cell models. We utilize the constrained structure of tumor proliferation models to design an accelerated lexicographic search algorithm for generating the optimal solution. For comparison purposes, we also designed two suboptimal search algorithms based on evolutionary algorithms and hill-climbing based techniques. Results over synthetic models and models generated from Genomics of Drug Sensitivity in Cancer database shows the ability of the proposed algorithms to arrive at optimal or close to optimal solutions in significantly lower number of steps as compared to exhaustive search. We also present the theoretical analysis of the expected number of comparisons required for the proposed Lexicographic search that compare favorably with the observed number of computations. Conclusions The proposed algorithms provide a framework for design of combination therapy that tackles tumor heterogeneity while satisfying toxicity constraints.
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Affiliation(s)
- Kevin Matlock
- Department of Electrical and Computer Engineering, Texas Tech University, 1012 Boston Ave, Lubbock, 79409, TX, USA
| | - Noah Berlow
- Children's Cancer Therapy Development Institute, Portland, 97005, OR, USA
| | - Charles Keller
- Children's Cancer Therapy Development Institute, Portland, 97005, OR, USA
| | - Ranadip Pal
- Department of Electrical and Computer Engineering, Texas Tech University, 1012 Boston Ave, Lubbock, 79409, TX, USA.
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Weiss A, Nowak-Sliwinska P. Current Trends in Multidrug Optimization: An Alley of Future Successful Treatment of Complex Disorders. SLAS Technol 2016; 22:254-275. [DOI: 10.1177/2472630316682338] [Citation(s) in RCA: 26] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/19/2022]
Abstract
The identification of effective and long-lasting cancer therapies still remains elusive, partially due to patient and tumor heterogeneity, acquired drug resistance, and single-drug dose-limiting toxicities. The use of drug combinations may help to overcome some limitations of current cancer therapies by challenging the robustness and redundancy of biological processes. However, effective drug combination optimization requires the careful consideration of numerous parameters. The complexity of this optimization problem is clearly nontrivial and likely requires the assistance of advanced heuristic optimization techniques. In the current review, we discuss the application of optimization techniques for the identification of optimal drug combinations. More specifically, we focus on the application of phenotype-based screening approaches in the field of cancer therapy. These methods are divided into three categories: (1) modeling methods, (2) model-free approaches based on biological search algorithms, and (3) merged approaches, particularly phenotypically driven network biology methods and computation network models relying on phenotypic data. In addition to a brief description of each approach, we include a critical discussion of the advantages and disadvantages of each method, with a strong focus on the limitations and considerations needed to successfully apply such methods in biological research.
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Affiliation(s)
- Andrea Weiss
- Institute of Chemical Sciences and Engineering, Swiss Federal Institute of Technology (EPFL), Lausanne, Switzerland
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13
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Weiss A, Nowak-Sliwinska P. Current Trends in Multidrug Optimization. JOURNAL OF LABORATORY AUTOMATION 2016:2211068216682338. [PMID: 28095178 DOI: 10.1177/2211068216682338] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/28/2024]
Abstract
The identification of effective and long-lasting cancer therapies still remains elusive, partially due to patient and tumor heterogeneity, acquired drug resistance, and single-drug dose-limiting toxicities. The use of drug combinations may help to overcome some limitations of current cancer therapies by challenging the robustness and redundancy of biological processes. However, effective drug combination optimization requires the careful consideration of numerous parameters. The complexity of this optimization problem is clearly nontrivial and likely requires the assistance of advanced heuristic optimization techniques. In the current review, we discuss the application of optimization techniques for the identification of optimal drug combinations. More specifically, we focus on the application of phenotype-based screening approaches in the field of cancer therapy. These methods are divided into three categories: (1) modeling methods, (2) model-free approaches based on biological search algorithms, and (3) merged approaches, particularly phenotypically driven network biology methods and computation network models relying on phenotypic data. In addition to a brief description of each approach, we include a critical discussion of the advantages and disadvantages of each method, with a strong focus on the limitations and considerations needed to successfully apply such methods in biological research.
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Affiliation(s)
- Andrea Weiss
- 1 Institute of Chemical Sciences and Engineering, Swiss Federal Institute of Technology (EPFL), Lausanne, Switzerland
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Design and Statistical Analysis of Multidrug Combinations in Preclinical Studies and Phase I Clinical Trials. ACTA ACUST UNITED AC 2016. [DOI: 10.1007/978-3-319-42568-9_18] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 04/12/2023]
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15
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Prediction of multidimensional drug dose responses based on measurements of drug pairs. Proc Natl Acad Sci U S A 2016; 113:10442-7. [PMID: 27562164 DOI: 10.1073/pnas.1606301113] [Citation(s) in RCA: 105] [Impact Index Per Article: 13.1] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/18/2022] Open
Abstract
Finding potent multidrug combinations against cancer and infections is a pressing therapeutic challenge; however, screening all combinations is difficult because the number of experiments grows exponentially with the number of drugs and doses. To address this, we present a mathematical model that predicts the effects of three or more antibiotics or anticancer drugs at all doses based only on measurements of drug pairs at a few doses, without need for mechanistic information. The model provides accurate predictions on available data for antibiotic combinations, and on experiments presented here on the response matrix of three cancer drugs at eight doses per drug. This approach offers a way to search for effective multidrug combinations using a small number of experiments.
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16
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Cross K, Craggs R, Swift D, Sitaram A, Daya S, Roberts M, Hawley S, Owen P, Isherwood B. Delivering an Automated and Integrated Approach to Combination Screening Using Acoustic-Droplet Technology. ACTA ACUST UNITED AC 2016; 21:143-52. [DOI: 10.1177/2211068215579163] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/07/2015] [Indexed: 02/06/2023]
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17
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Nowak-Sliwinska P, Weiss A, Ding X, Dyson PJ, van den Bergh H, Griffioen AW, Ho CM. Optimization of drug combinations using Feedback System Control. Nat Protoc 2016; 11:302-15. [PMID: 26766116 DOI: 10.1038/nprot.2016.017] [Citation(s) in RCA: 69] [Impact Index Per Article: 8.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/14/2023]
Abstract
We describe a protocol for the discovery of synergistic drug combinations for the treatment of disease. Synergistic drug combinations lead to the use of drugs at lower doses, which reduces side effects and can potentially lead to reduced drug resistance, while being clinically more effective than the individual drugs. To cope with the extremely large search space for these combinations, we developed an efficient combinatorial drug screening method called the Feedback System Control (FSC) technique. Starting with a broad selection of drugs, the method follows an iterative approach of experimental testing in a relevant bioassay and analysis of the results by FSC. First, the protocol uses a cell viability assay to generate broad dose-response curves to assess the efficacy of individual compounds. These curves are then used to guide the dosage input of each drug to be tested in combination. Data from applied drug combinations are input into the differential evolution (DE) algorithm, which predicts new combinations to be tested in vitro. This process identifies optimal drug-dose combinations, while saving orders of magnitude in experimental effort. The complete optimization process is estimated to take ∼4 weeks. FSC does not require insight into the disease mechanism, and it has therefore been applied to find combination therapies for many different pathologies, including cancer and infectious diseases, and it has also been used in organ transplantation.
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Affiliation(s)
- Patrycja Nowak-Sliwinska
- Department of Medical Oncology, Angiogenesis Laboratory, Vrije Universiteit (VU) University Medical Center, Amsterdam, the Netherlands
| | - Andrea Weiss
- Department of Medical Oncology, Angiogenesis Laboratory, Vrije Universiteit (VU) University Medical Center, Amsterdam, the Netherlands
| | - Xianting Ding
- Med-X Research Institute, School of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai, China
| | - Paul J Dyson
- Institute of Chemical Sciences and Engineering, Swiss Federal Institute of Technology Lausanne (EPFL), Lausanne, Switzerland
| | - Hubert van den Bergh
- Institute of Chemical Sciences and Engineering, Swiss Federal Institute of Technology Lausanne (EPFL), Lausanne, Switzerland
| | - Arjan W Griffioen
- Department of Medical Oncology, Angiogenesis Laboratory, Vrije Universiteit (VU) University Medical Center, Amsterdam, the Netherlands
| | - Chih-Ming Ho
- Department of Mechanical and Aerospace Engineering, University of California, Los Angeles, California, USA
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18
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Mizeranschi A, Groen D, Borgdorff J, Hoekstra AG, Chopard B, Dubitzky W. Anatomy and Physiology of Multiscale Modeling and Simulation in Systems Medicine. Methods Mol Biol 2016; 1386:375-404. [PMID: 26677192 DOI: 10.1007/978-1-4939-3283-2_17] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/05/2023]
Abstract
Systems medicine is the application of systems biology concepts, methods, and tools to medical research and practice. It aims to integrate data and knowledge from different disciplines into biomedical models and simulations for the understanding, prevention, cure, and management of complex diseases. Complex diseases arise from the interactions among disease-influencing factors across multiple levels of biological organization from the environment to molecules. To tackle the enormous challenges posed by complex diseases, we need a modeling and simulation framework capable of capturing and integrating information originating from multiple spatiotemporal and organizational scales. Multiscale modeling and simulation in systems medicine is an emerging methodology and discipline that has already demonstrated its potential in becoming this framework. The aim of this chapter is to present some of the main concepts, requirements, and challenges of multiscale modeling and simulation in systems medicine.
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Affiliation(s)
- Alexandru Mizeranschi
- Biomedical Sciences Research Institute, University of Ulster, Cromore Road, Coleraine, Co. Londonderry, BT52 1SA, UK
| | - Derek Groen
- Chemistry Department, Centre for Computational Science, University College London, 20 Gordon Street, WC1H 0AJ, London, UK
| | - Joris Borgdorff
- Netherlands eScience Center, Science Park 140, 1098 XG, Amsterdam, The Netherlands
| | - Alfons G Hoekstra
- Computational Science Lab, Institute for Informatics, Faculty of Science, University of Amsterdam, Sciencepark 904, 1098 XH, Amsterdam, The Netherlands
- Advanced Computing Lab, ITMO University, 197101, 49 Kronverkskiy av., St. Petersburg, Russia
| | - Bastien Chopard
- Computer Science Department, University of Geneva, 7 route de Drize, 1227, Carouge, Switzerland
| | - Werner Dubitzky
- Biomedical Sciences Research Institute, University of Ulster, Cromore Road, Coleraine, Co. Londonderry, BT52 1SA, UK.
- School of Biomedical Sciences, University of Ulster, Coleraine campus, Cromore Road, Coleraine, Co. Londonderry, BT52 1SA, UK.
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19
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Optimization Methodologies for the Production of Pharmaceutical Products. METHODS IN PHARMACOLOGY AND TOXICOLOGY 2016. [DOI: 10.1007/978-1-4939-2996-2_9] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/29/2023]
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20
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Kashif M, Andersson C, Hassan S, Karlsson H, Senkowski W, Fryknäs M, Nygren P, Larsson R, Gustafsson M. In vitro discovery of promising anti-cancer drug combinations using iterative maximisation of a therapeutic index. Sci Rep 2015; 5:14118. [PMID: 26392291 PMCID: PMC4585751 DOI: 10.1038/srep14118] [Citation(s) in RCA: 23] [Impact Index Per Article: 2.6] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/12/2015] [Accepted: 08/18/2015] [Indexed: 02/03/2023] Open
Abstract
In vitro-based search for promising anti-cancer drug combinations may provide important leads to improved cancer therapies. Currently there are no integrated computational-experimental methods specifically designed to search for combinations, maximizing a predefined therapeutic index (TI) defined in terms of appropriate model systems. Here, such a pipeline is presented allowing the search for optimal combinations among an arbitrary number of drugs while also taking experimental variability into account. The TI optimized is the cytotoxicity difference (in vitro) between a target model and an adverse side effect model. Focusing on colorectal carcinoma (CRC), the pipeline provided several combinations that are effective in six different CRC models with limited cytotoxicity in normal cell models. Herein we describe the identification of the combination (Trichostatin A, Afungin, 17-AAG) and present results from subsequent characterisations, including efficacy in primary cultures of tumour cells from CRC patients. We hypothesize that its effect derives from potentiation of the proteotoxic action of 17-AAG by Trichostatin A and Afungin. The discovered drug combinations against CRC are significant findings themselves and also indicate that the proposed strategy has great potential for suggesting drug combination treatments suitable for other cancer types as well as for other complex diseases.
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Affiliation(s)
- M. Kashif
- Uppsala University, Dept of Medical Sciences, Cancer Pharmacology and Computational Medicine, Akademiska Sjukhuset, SE-751 85 Uppsala, Sweden
| | - C. Andersson
- Uppsala University, Dept of Medical Sciences, Cancer Pharmacology and Computational Medicine, Akademiska Sjukhuset, SE-751 85 Uppsala, Sweden
| | - S. Hassan
- Uppsala University, Dept of Medical Sciences, Cancer Pharmacology and Computational Medicine, Akademiska Sjukhuset, SE-751 85 Uppsala, Sweden
| | - H. Karlsson
- Uppsala University, Dept of Medical Sciences, Cancer Pharmacology and Computational Medicine, Akademiska Sjukhuset, SE-751 85 Uppsala, Sweden
| | - W. Senkowski
- Uppsala University, Dept of Medical Sciences, Cancer Pharmacology and Computational Medicine, Akademiska Sjukhuset, SE-751 85 Uppsala, Sweden
| | - M. Fryknäs
- Uppsala University, Dept of Medical Sciences, Cancer Pharmacology and Computational Medicine, Akademiska Sjukhuset, SE-751 85 Uppsala, Sweden
| | - P. Nygren
- Uppsala University, Dept of Immunology, Genetics and Pathology (Experimental and Clinical Oncology), Akademiska Sjukhuset, SE-751 85 Uppsala, Sweden
| | - R. Larsson
- Uppsala University, Dept of Medical Sciences, Cancer Pharmacology and Computational Medicine, Akademiska Sjukhuset, SE-751 85 Uppsala, Sweden
| | - M.G. Gustafsson
- Uppsala University, Dept of Medical Sciences, Cancer Pharmacology and Computational Medicine, Akademiska Sjukhuset, SE-751 85 Uppsala, Sweden
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21
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Liu Q, Zhang C, Ding X, Deng H, Zhang D, Cui W, Xu H, Wang Y, Xu W, Lv L, Zhang H, He Y, Wu Q, Szyf M, Ho CM, Zhu J. Preclinical optimization of a broad-spectrum anti-bladder cancer tri-drug regimen via the Feedback System Control (FSC) platform. Sci Rep 2015; 5:11464. [PMID: 26088171 PMCID: PMC5155572 DOI: 10.1038/srep11464] [Citation(s) in RCA: 12] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/19/2015] [Accepted: 05/22/2015] [Indexed: 12/18/2022] Open
Abstract
Therapeutic outcomes of combination chemotherapy have not significantly advanced during the past decades. This has been attributed to the formidable challenges of optimizing drug combinations. Testing a matrix of all possible combinations of doses and agents in a single cell line is unfeasible due to the virtually infinite number of possibilities. We utilized the Feedback System Control (FSC) platform, a phenotype oriented approach to test 100 options among 15,625 possible combinations in four rounds of assaying to identify an optimal tri-drug combination in eight distinct chemoresistant bladder cancer cell lines. This combination killed between 82.86% and 99.52% of BCa cells, but only 47.47% of the immortalized benign bladder epithelial cells. Preclinical in vivo verification revealed its markedly enhanced anti-tumor efficacy as compared to its bi- or mono-drug components in cell line-derived tumor xenografts. The collective response of these pathways to component drugs was both cell type- and drug type specific. However, the entire spectrum of pathways triggered by the tri-drug regimen was similar in all four cancer cell lines, explaining its broad spectrum killing of BCa lines, which did not occur with its component drugs. Our findings here suggest that the FSC platform holdspromise for optimization of anti-cancer combination chemotherapy.
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Affiliation(s)
- Qi Liu
- School of Life Science and Technology, State Key Laboratory of Urban Water Resource and Environment, Harbin Institute of Technology, Harbin, Heilongjiang 150001, China, and Department of Anatomy and Cell Biology, University of Iowa, Carver College of Medicine, Iowa City, IA 52242, USA
| | - Cheng Zhang
- Department of Urology, The First Affiliated Hospital of Harbin Medical University, Harbin, Heilongjiang Province, China
| | - Xianting Ding
- Med-X Research Institute, School of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai, 200030, China
| | - Hui Deng
- Cancer Epigenetics Program, Anhui Cancer Hospital, Hefei, Anhui 230031, China
| | - Daming Zhang
- Department of Neurosurgery, The First Affiliated Hospital of Harbin Medical University, Harbin, Heilongjiang Province, China
| | - Wei Cui
- School of Life Science and Technology, State Key Laboratory of Urban Water Resource and Environment, Harbin Institute of Technology, Harbin, Heilongjiang 150001, China, and Department of Anatomy and Cell Biology, University of Iowa, Carver College of Medicine, Iowa City, IA 52242, USA
| | - Hongwei Xu
- Department of Pathology, The First Affiliated Hospital of Harbin Medical University, Harbin, Heilongjiang Province, China
| | - Yingwei Wang
- Department of Urology, The First Affiliated Hospital of Harbin Medical University, Harbin, Heilongjiang Province, China
| | - Wanhai Xu
- Department of Urology, The Forth Affiliated Hospital of Harbin Medical University, Harbin, Heilongjiang Province, China
| | - Lei Lv
- Cancer Epigenetics Program, Anhui Cancer Hospital, Hefei, Anhui 230031, China
| | - Hongyu Zhang
- Cancer Epigenetics Program, Shanghai Cancer Institute, Renji Hospital, Shanghai Jiaotong University, Shanghai 200032, China
| | - Yinghua He
- Cancer Epigenetics Program, Shanghai Cancer Institute, Renji Hospital, Shanghai Jiaotong University, Shanghai 200032, China
| | - Qiong Wu
- School of Life Science and Technology, State Key Laboratory of Urban Water Resource and Environment, Harbin Institute of Technology, Harbin, Heilongjiang 150001, China, and Department of Anatomy and Cell Biology, University of Iowa, Carver College of Medicine, Iowa City, IA 52242, USA
| | - Moshe Szyf
- Department of Pharmacology and Therapeutics McGill University Medical School 3655 Sir William Osler Promenade #1309, Montreal, Quebec Canada
| | - Chih-Ming Ho
- Mechanical and Aerospace Engineering Department, Biomedical Engineering Department, University of California, Los Angeles, CA 90095-1597, USA
| | - Jingde Zhu
- 1] Cancer Epigenetics Program, Anhui Cancer Hospital, Hefei, Anhui 230031, China [2] Cancer Epigenetics Program, Shanghai Cancer Institute, Renji Hospital, Shanghai Jiaotong University, Shanghai 200032, China
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22
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Ligeti B, Pénzváltó Z, Vera R, Győrffy B, Pongor S. A Network-Based Target Overlap Score for Characterizing Drug Combinations: High Correlation with Cancer Clinical Trial Results. PLoS One 2015; 10:e0129267. [PMID: 26047322 PMCID: PMC4457853 DOI: 10.1371/journal.pone.0129267] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/31/2014] [Accepted: 05/06/2015] [Indexed: 12/27/2022] Open
Abstract
Drug combinations are highly efficient in systemic treatment of complex multigene diseases such as cancer, diabetes, arthritis and hypertension. Most currently used combinations were found in empirical ways, which limits the speed of discovery for new and more effective combinations. Therefore, there is a substantial need for efficient and fast computational methods. Here, we present a principle that is based on the assumption that perturbations generated by multiple pharmaceutical agents propagate through an interaction network and can cause unexpected amplification at targets not immediately affected by the original drugs. In order to capture this phenomenon, we introduce a novel Target Overlap Score (TOS) that is defined for two pharmaceutical agents as the number of jointly perturbed targets divided by the number of all targets potentially affected by the two agents. We show that this measure is correlated with the known effects of beneficial and deleterious drug combinations taken from the DCDB, TTD and Drugs.com databases. We demonstrate the utility of TOS by correlating the score to the outcome of recent clinical trials evaluating trastuzumab, an effective anticancer agent utilized in combination with anthracycline- and taxane- based systemic chemotherapy in HER2-receptor (erb-b2 receptor tyrosine kinase 2) positive breast cancer.
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Affiliation(s)
- Balázs Ligeti
- Faculty of Information Technology, Pázmány Péter Catholic University, Budapest, Hungary
| | - Zsófia Pénzváltó
- MTA TTK Lendület Cancer Biomarker Research Group, Budapest, Hungary
| | - Roberto Vera
- Faculty of Information Technology, Pázmány Péter Catholic University, Budapest, Hungary
- Protein Structure and Bioinformatics Group, International Centre for Genetic Engineering and Biotechnology, Trieste, Italy
| | - Balázs Győrffy
- MTA TTK Lendület Cancer Biomarker Research Group, Budapest, Hungary
- 2nd Department of Pediatrics, Semmelweis University, Budapest, Hungary
- MTA-SE Pediatrics and Nephrology Research Group, Budapest, Hungary
- * E-mail: (BG); (SP)
| | - Sándor Pongor
- Faculty of Information Technology, Pázmány Péter Catholic University, Budapest, Hungary
- Protein Structure and Bioinformatics Group, International Centre for Genetic Engineering and Biotechnology, Trieste, Italy
- * E-mail: (BG); (SP)
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23
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Weiss A, Ding X, van Beijnum JR, Wong I, Wong TJ, Berndsen RH, Dormond O, Dallinga M, Shen L, Schlingemann RO, Pili R, Ho CM, Dyson PJ, van den Bergh H, Griffioen AW, Nowak-Sliwinska P. Rapid optimization of drug combinations for the optimal angiostatic treatment of cancer. Angiogenesis 2015; 18:233-44. [PMID: 25824484 PMCID: PMC4473022 DOI: 10.1007/s10456-015-9462-9] [Citation(s) in RCA: 93] [Impact Index Per Article: 10.3] [Reference Citation Analysis] [Abstract] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/25/2015] [Accepted: 03/13/2015] [Indexed: 01/13/2023]
Abstract
Drug combinations can improve angiostatic cancer treatment efficacy and enable the reduction of side effects and drug resistance. Combining drugs is non-trivial due to the high number of possibilities. We applied a feedback system control (FSC) technique with a population-based stochastic search algorithm to navigate through the large parametric space of nine angiostatic drugs at four concentrations to identify optimal low-dose drug combinations. This implied an iterative approach of in vitro testing of endothelial cell viability and algorithm-based analysis. The optimal synergistic drug combination, containing erlotinib, BEZ-235 and RAPTA-C, was reached in a small number of iterations. Final drug combinations showed enhanced endothelial cell specificity and synergistically inhibited proliferation (p < 0.001), but not migration of endothelial cells, and forced enhanced numbers of endothelial cells to undergo apoptosis (p < 0.01). Successful translation of this drug combination was achieved in two preclinical in vivo tumor models. Tumor growth was inhibited synergistically and significantly (p < 0.05 and p < 0.01, respectively) using reduced drug doses as compared to optimal single-drug concentrations. At the applied conditions, single-drug monotherapies had no or negligible activity in these models. We suggest that FSC can be used for rapid identification of effective, reduced dose, multi-drug combinations for the treatment of cancer and other diseases.
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Affiliation(s)
- Andrea Weiss
- Institute of Chemical Sciences and Engineering, Swiss Federal Institute of Technology (EPFL), 1015, Lausanne, Switzerland
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24
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Goswami CP, Cheng L, Alexander PS, Singal A, Li L. A New Drug Combinatory Effect Prediction Algorithm on the Cancer Cell Based on Gene Expression and Dose-Response Curve. CPT-PHARMACOMETRICS & SYSTEMS PHARMACOLOGY 2015. [PMID: 26225234 PMCID: PMC4360667 DOI: 10.1002/psp4.9] [Citation(s) in RCA: 24] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 12/14/2022]
Abstract
Gene expression data before and after treatment with an individual drug and the IC20 of dose–response data were utilized to predict two drugs' interaction effects on a diffuse large B-cell lymphoma (DLBCL) cancer cell. A novel drug interaction scoring algorithm was developed to account for either synergistic or antagonistic effects between drug combinations. Different core gene selection schemes were investigated, which included the whole gene set, the drug-sensitive gene set, the drug-sensitive minus drug-resistant gene set, and the known drug target gene set. The prediction scores were compared with the observed drug interaction data at 6, 12, and 24 hours with a probability concordance (PC) index. The test result shows the concordance between observed and predicted drug interaction ranking reaches a PC index of 0.605. The scoring reliability and efficiency was further confirmed in five drug interaction studies published in the GEO database.
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Affiliation(s)
- C Pankaj Goswami
- Molecular Lab, Thomas Jefferson University Hospitals Philadelphia, Pennsylvania, USA
| | - L Cheng
- Centers for Computational Biology and Bioinformatics, School of Medicine, Indiana University Indianapolis, Indiana, USA ; Department of Medical and Molecular Genetics, School of Medicine, Indiana University Indianapolis, Indiana, USA ; State Key Laboratory of Oncogenes and Related Genes, Shanghai Cancer Institute Shanghai, China
| | - P S Alexander
- Centers for Computational Biology and Bioinformatics, School of Medicine, Indiana University Indianapolis, Indiana, USA
| | - A Singal
- Centers for Computational Biology and Bioinformatics, School of Medicine, Indiana University Indianapolis, Indiana, USA
| | - L Li
- Centers for Computational Biology and Bioinformatics, School of Medicine, Indiana University Indianapolis, Indiana, USA ; Department of Medical and Molecular Genetics, School of Medicine, Indiana University Indianapolis, Indiana, USA
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25
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Wang X, Ma J, Li X, Zhao X, Lin Z, Chen J, Shao Z. Optimization of Chemical Fungicide Combinations Targeting the Maize Fungal Pathogen, Bipolaris maydis: A Systematic Quantitative Approach. IEEE Trans Biomed Eng 2015; 62:80-7. [DOI: 10.1109/tbme.2014.2339295] [Citation(s) in RCA: 12] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/08/2022]
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26
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Angus SD, Piotrowska MJ. A matter of timing: identifying significant multi-dose radiotherapy improvements by numerical simulation and genetic algorithm search. PLoS One 2014; 9:e114098. [PMID: 25460164 PMCID: PMC4252029 DOI: 10.1371/journal.pone.0114098] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/15/2014] [Accepted: 11/01/2014] [Indexed: 12/25/2022] Open
Abstract
Multi-dose radiotherapy protocols (fraction dose and timing) currently used in the clinic are the product of human selection based on habit, received wisdom, physician experience and intra-day patient timetabling. However, due to combinatorial considerations, the potential treatment protocol space for a given total dose or treatment length is enormous, even for relatively coarse search; well beyond the capacity of traditional in-vitro methods. In constrast, high fidelity numerical simulation of tumor development is well suited to the challenge. Building on our previous single-dose numerical simulation model of EMT6/Ro spheroids, a multi-dose irradiation response module is added and calibrated to the effective dose arising from 18 independent multi-dose treatment programs available in the experimental literature. With the developed model a constrained, non-linear, search for better performing cadidate protocols is conducted within the vicinity of two benchmarks by genetic algorithm (GA) techniques. After evaluating less than 0.01% of the potential benchmark protocol space, candidate protocols were identified by the GA which conferred an average of 9.4% (max benefit 16.5%) and 7.1% (13.3%) improvement (reduction) on tumour cell count compared to the two benchmarks, respectively. Noticing that a convergent phenomenon of the top performing protocols was their temporal synchronicity, a further series of numerical experiments was conducted with periodic time-gap protocols (10 h to 23 h), leading to the discovery that the performance of the GA search candidates could be replicated by 17-18 h periodic candidates. Further dynamic irradiation-response cell-phase analysis revealed that such periodicity cohered with latent EMT6/Ro cell-phase temporal patterning. Taken together, this study provides powerful evidence towards the hypothesis that even simple inter-fraction timing variations for a given fractional dose program may present a facile, and highly cost-effecitive means of significantly improving clinical efficacy.
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Affiliation(s)
- Simon D. Angus
- Department of Economics, Monash University, Melbourne, Victoria, Australia
| | - Monika Joanna Piotrowska
- Faculty of Mathematics Informatics and Mechanics, Institute of Applied Mathematics and Mechanics, University of Warsaw, Warsaw, Mazowieckie, Poland
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27
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Nagai N, Kaji H, Onami H, Katsukura Y, Ishikawa Y, Nezhad ZK, Sampei K, Iwata S, Ito S, Nishizawa M, Nakazawa T, Osumi N, Mashima Y, Abe T. A platform for controlled dual-drug delivery to the retina: protective effects against light-induced retinal damage in rats. Adv Healthc Mater 2014; 3:1555-60, 1524. [PMID: 24753450 DOI: 10.1002/adhm.201400114] [Citation(s) in RCA: 17] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/24/2014] [Revised: 03/26/2014] [Indexed: 11/12/2022]
Abstract
Controlled transscleral co-delivery of two drugs, edaravone (EDV) and unoprostone (UNO), using a platform that comprises a microfabricated reservoir, controlled-release cover, and drug formulations, which are made of photopolymerized poly(ethyleneglycol) dimethacrylates, shows synergistic retinal neuroprotection against light injury in rats when compared with single-drug-loaded devices. The device would offer a safer therapeutic method than intravitreal injections for retinal disease treatments.
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Affiliation(s)
- Nobuhiro Nagai
- Division of Clinical Cell Therapy, United Centers for Advanced Research and Translational Medicine (ART); Tohoku University Graduate School of Medicine; 2-1 Seiryo-machi Aoba-ku, Sendai 980-8575 Japan
| | - Hirokazu Kaji
- Department of Bioengineering and Robotics; Graduate School of Engineering, Tohoku University; 6-6-01 Aramaki Aoba-ku, Sendai 980-8579 Japan
| | - Hideyuki Onami
- Division of Clinical Cell Therapy, United Centers for Advanced Research and Translational Medicine (ART); Tohoku University Graduate School of Medicine; 2-1 Seiryo-machi Aoba-ku, Sendai 980-8575 Japan
- Department of Ophthalmology; Tohoku University Graduate School of Medicine; 1-1 Seiryo-machi Aoba-ku, Sendai 980-8574 Japan
| | - Yuki Katsukura
- Division of Clinical Cell Therapy, United Centers for Advanced Research and Translational Medicine (ART); Tohoku University Graduate School of Medicine; 2-1 Seiryo-machi Aoba-ku, Sendai 980-8575 Japan
| | - Yumi Ishikawa
- Division of Clinical Cell Therapy, United Centers for Advanced Research and Translational Medicine (ART); Tohoku University Graduate School of Medicine; 2-1 Seiryo-machi Aoba-ku, Sendai 980-8575 Japan
| | - Zhaleh Kashkouli Nezhad
- Division of Clinical Cell Therapy, United Centers for Advanced Research and Translational Medicine (ART); Tohoku University Graduate School of Medicine; 2-1 Seiryo-machi Aoba-ku, Sendai 980-8575 Japan
| | - Kaori Sampei
- Division of Clinical Cell Therapy, United Centers for Advanced Research and Translational Medicine (ART); Tohoku University Graduate School of Medicine; 2-1 Seiryo-machi Aoba-ku, Sendai 980-8575 Japan
| | - Satoru Iwata
- Division of Clinical Cell Therapy, United Centers for Advanced Research and Translational Medicine (ART); Tohoku University Graduate School of Medicine; 2-1 Seiryo-machi Aoba-ku, Sendai 980-8575 Japan
| | - Shuntaro Ito
- Department of Bioengineering and Robotics; Graduate School of Engineering, Tohoku University; 6-6-01 Aramaki Aoba-ku, Sendai 980-8579 Japan
| | - Matsuhiko Nishizawa
- Department of Bioengineering and Robotics; Graduate School of Engineering, Tohoku University; 6-6-01 Aramaki Aoba-ku, Sendai 980-8579 Japan
| | - Toru Nakazawa
- Department of Ophthalmology; Tohoku University Graduate School of Medicine; 1-1 Seiryo-machi Aoba-ku, Sendai 980-8574 Japan
| | - Noriko Osumi
- Division of Developmental Neuroscience; United Centers for Advanced Research and Translational Medicine (ART); Tohoku University Graduate School of Medicine; 2-1 Seiryo-machi Aoba-ku, Sendai 980-8575 Japan
| | - Yukihiko Mashima
- R-tech Ueno Ltd.; 1-1-7, Uchisaiwai-cho Chiyoda-ku, Tokyo 100-0011 Japan
| | - Toshiaki Abe
- Division of Clinical Cell Therapy, United Centers for Advanced Research and Translational Medicine (ART); Tohoku University Graduate School of Medicine; 2-1 Seiryo-machi Aoba-ku, Sendai 980-8575 Japan
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Kang Y, Hodges A, Ong E, Roberts W, Piermarocchi C, Paternostro G. Identification of drug combinations containing imatinib for treatment of BCR-ABL+ leukemias. PLoS One 2014; 9:e102221. [PMID: 25029499 PMCID: PMC4100887 DOI: 10.1371/journal.pone.0102221] [Citation(s) in RCA: 15] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/22/2014] [Accepted: 06/16/2014] [Indexed: 11/18/2022] Open
Abstract
The BCR-ABL translocation is found in chronic myeloid leukemia (CML) and in Ph+ acute lymphoblastic leukemia (ALL) patients. Although imatinib and its analogues have been used as front-line therapy to target this mutation and control the disease for over a decade, resistance to the therapy is still observed and most patients are not cured but need to continue the therapy indefinitely. It is therefore of great importance to find new therapies, possibly as drug combinations, which can overcome drug resistance. In this study, we identified eleven candidate anti-leukemic drugs that might be combined with imatinib, using three approaches: a kinase inhibitor library screen, a gene expression correlation analysis, and literature analysis. We then used an experimental search algorithm to efficiently explore the large space of possible drug and dose combinations and identified drug combinations that selectively kill a BCR-ABL+ leukemic cell line (K562) over a normal fibroblast cell line (IMR-90). Only six iterations of the algorithm were needed to identify very selective drug combinations. The efficacy of the top forty-nine combinations was further confirmed using Ph+ and Ph- ALL patient cells, including imatinib-resistant cells. Collectively, the drug combinations and methods we describe might be a first step towards more effective interventions for leukemia patients, especially those with the BCR-ABL translocation.
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MESH Headings
- Algorithms
- Antineoplastic Agents/administration & dosage
- Antineoplastic Agents/pharmacology
- Antineoplastic Agents/therapeutic use
- Antineoplastic Combined Chemotherapy Protocols
- Benzamides/administration & dosage
- Benzamides/pharmacology
- Benzamides/therapeutic use
- Cell Line, Tumor
- Dose-Response Relationship, Drug
- Drug Discovery
- Drug Resistance, Neoplasm
- Fusion Proteins, bcr-abl/metabolism
- Humans
- Imatinib Mesylate
- Leukemia, Myelogenous, Chronic, BCR-ABL Positive/drug therapy
- Leukemia, Myelogenous, Chronic, BCR-ABL Positive/metabolism
- Leukemia, Myelogenous, Chronic, BCR-ABL Positive/pathology
- Piperazines/administration & dosage
- Piperazines/pharmacology
- Piperazines/therapeutic use
- Pyrimidines/administration & dosage
- Pyrimidines/pharmacology
- Pyrimidines/therapeutic use
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Affiliation(s)
- Yunyi Kang
- Sanford-Burnham Medical Research Institute, La Jolla, California, United States of America
| | - Andrew Hodges
- Sanford-Burnham Medical Research Institute, La Jolla, California, United States of America
| | - Edison Ong
- Salgomed Inc., Del Mar, California, United States of America
| | - William Roberts
- Rady Children's Hospital, Department of Pediatrics, University of California San Diego, San Diego, California, United States of America
| | - Carlo Piermarocchi
- Department of Physics and Astronomy, Michigan State University, East Lansing, Michigan, United States of America
| | - Giovanni Paternostro
- Sanford-Burnham Medical Research Institute, La Jolla, California, United States of America
- * E-mail:
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Computational analysis of image-based drug profiling predicts synergistic drug combinations: applications in triple-negative breast cancer. Mol Oncol 2014; 8:1548-60. [PMID: 24997502 DOI: 10.1016/j.molonc.2014.06.007] [Citation(s) in RCA: 12] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/21/2014] [Accepted: 06/10/2014] [Indexed: 12/31/2022] Open
Abstract
An imaged-based profiling and analysis system was developed to predict clinically effective synergistic drug combinations that could accelerate the identification of effective multi-drug therapies for the treatment of triple-negative breast cancer and other challenging malignancies. The identification of effective drug combinations for the treatment of triple-negative breast cancer (TNBC) was achieved by integrating high-content screening, computational analysis, and experimental biology. The approach was based on altered cellular phenotypes induced by 55 FDA-approved drugs and biologically active compounds, acquired using fluorescence microscopy and retained in multivariate compound profiles. Dissimilarities between compound profiles guided the identification of 5 combinations, which were assessed for qualitative interaction on TNBC cell growth. The combination of the microtubule-targeting drug vinblastine with KSP/Eg5 motor protein inhibitors monastrol or ispinesib showed potent synergism in 3 independent TNBC cell lines, which was not substantiated in normal fibroblasts. The synergistic interaction was mediated by an increase in mitotic arrest with cells demonstrating typical ispinesib-induced monopolar mitotic spindles, which translated into enhanced apoptosis induction. The antitumour activity of the combination vinblastine/ispinesib was confirmed in an orthotopic mouse model of TNBC. Compared to single drug treatment, combination treatment significantly reduced tumour growth without causing increased toxicity. Image-based profiling and analysis led to the rapid discovery of a drug combination effective against TNBC in vitro and in vivo, and has the potential to lead to the development of new therapeutic options in other hard-to-treat cancers.
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A diverse stochastic search algorithm for combination therapeutics. BIOMED RESEARCH INTERNATIONAL 2014; 2014:873436. [PMID: 24738075 PMCID: PMC3971504 DOI: 10.1155/2014/873436] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 11/17/2013] [Revised: 01/20/2014] [Accepted: 02/06/2014] [Indexed: 11/26/2022]
Abstract
Background. Design of drug combination cocktails to maximize sensitivity for individual patients presents a challenge in terms of minimizing the number of experiments to attain the desired objective. The enormous number of possible drug combinations constrains exhaustive experimentation approaches, and personal variations in genetic diseases restrict the use of prior knowledge in optimization. Results. We present a stochastic search algorithm that consisted of a parallel experimentation phase followed by a combination of focused and diversified sequential search. We evaluated our approach on seven synthetic examples; four of them were evaluated twice with different parameters, and two biological examples of bacterial and lung cancer cell inhibition response to combination drugs. The performance of our approach as compared to recently proposed adaptive reference update approach was superior for all the examples considered, achieving an average of 45% reduction in the number of experimental iterations. Conclusions. As the results illustrate, the proposed diverse stochastic search algorithm can produce optimized combinations in relatively smaller number of iterative steps. This approach can be combined with available knowledge on the genetic makeup of the patient to design optimal selection of drug cocktails.
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Peng H, Peng T, Wen J, Engler DA, Matsunami RK, Su J, Zhang L, Chang CCJ, Zhou X. Characterization of p38 MAPK isoforms for drug resistance study using systems biology approach. ACTA ACUST UNITED AC 2014; 30:1899-907. [PMID: 24618474 DOI: 10.1093/bioinformatics/btu133] [Citation(s) in RCA: 26] [Impact Index Per Article: 2.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/09/2023]
Abstract
MOTIVATION p38 mitogen-activated protein kinase activation plays an important role in resistance to chemotherapeutic cytotoxic drugs in treating multiple myeloma (MM). However, how the p38 mitogen-activated protein kinase signaling pathway is involved in drug resistance, in particular the roles that the various p38 isoforms play, remains largely unknown. METHOD To explore the underlying mechanisms, we developed a novel systems biology approach by integrating liquid chromatography-mass spectrometry and reverse phase protein array data from human MM cell lines with computational pathway models in which the unknown parameters were inferred using a proposed novel algorithm called modularized factor graph. RESULTS New mechanisms predicted by our models suggest that combined activation of various p38 isoforms may result in drug resistance in MM via regulating the related pathways including extracellular signal-regulated kinase (ERK) pathway and NFкB pathway. ERK pathway regulating cell growth is synergistically regulated by p38δ isoform, whereas nuclear factor kappa B (NFкB) pathway regulating cell apoptosis is synergistically regulated by p38α isoform. This finding that p38δ isoform promotes the phosphorylation of ERK1/2 in MM cells treated with bortezomib was validated by western blotting. Based on the predicted mechanisms, we further screened drug combinations in silico and found that a promising drug combination targeting ERK1/2 and NFκB might reduce the effects of drug resistance in MM cells. This study provides a framework of a systems biology approach to studying drug resistance and drug combination selection. AVAILABILITY AND IMPLEMENTATION RPPA experimental Data and Matlab source codes of modularized factor graph for parameter estimation are freely available online at http://ctsb.is.wfubmc.edu/publications/modularized-factor-graph.php.
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Affiliation(s)
- Huiming Peng
- Center for Bioinformatics & Systems Biology and Department of Radiology, Wake Forest School of Medicine, Winston-Salem, NC 27157, USA, Department of Radiology, The Methodist Hospital Research Institute, Houston, TX 77030, USA, Department of Pathology, The Methodist Hospital Research Institute, Houston, TX 77030, USA, Proteomics Programmatic Core Laboratory, The Methodist Hospital Research Institute, Houston, TX 77030, USA, College of Computer and Information Science, Southwest University, Chongqing 400715, China, Department of Pathology, Florida Hospital, Orlando, FL 32803, USA
| | - Tao Peng
- Center for Bioinformatics & Systems Biology and Department of Radiology, Wake Forest School of Medicine, Winston-Salem, NC 27157, USA, Department of Radiology, The Methodist Hospital Research Institute, Houston, TX 77030, USA, Department of Pathology, The Methodist Hospital Research Institute, Houston, TX 77030, USA, Proteomics Programmatic Core Laboratory, The Methodist Hospital Research Institute, Houston, TX 77030, USA, College of Computer and Information Science, Southwest University, Chongqing 400715, China, Department of Pathology, Florida Hospital, Orlando, FL 32803, USA
| | - Jianguo Wen
- Center for Bioinformatics & Systems Biology and Department of Radiology, Wake Forest School of Medicine, Winston-Salem, NC 27157, USA, Department of Radiology, The Methodist Hospital Research Institute, Houston, TX 77030, USA, Department of Pathology, The Methodist Hospital Research Institute, Houston, TX 77030, USA, Proteomics Programmatic Core Laboratory, The Methodist Hospital Research Institute, Houston, TX 77030, USA, College of Computer and Information Science, Southwest University, Chongqing 400715, China, Department of Pathology, Florida Hospital, Orlando, FL 32803, USA
| | - David A Engler
- Center for Bioinformatics & Systems Biology and Department of Radiology, Wake Forest School of Medicine, Winston-Salem, NC 27157, USA, Department of Radiology, The Methodist Hospital Research Institute, Houston, TX 77030, USA, Department of Pathology, The Methodist Hospital Research Institute, Houston, TX 77030, USA, Proteomics Programmatic Core Laboratory, The Methodist Hospital Research Institute, Houston, TX 77030, USA, College of Computer and Information Science, Southwest University, Chongqing 400715, China, Department of Pathology, Florida Hospital, Orlando, FL 32803, USA
| | - Risë K Matsunami
- Center for Bioinformatics & Systems Biology and Department of Radiology, Wake Forest School of Medicine, Winston-Salem, NC 27157, USA, Department of Radiology, The Methodist Hospital Research Institute, Houston, TX 77030, USA, Department of Pathology, The Methodist Hospital Research Institute, Houston, TX 77030, USA, Proteomics Programmatic Core Laboratory, The Methodist Hospital Research Institute, Houston, TX 77030, USA, College of Computer and Information Science, Southwest University, Chongqing 400715, China, Department of Pathology, Florida Hospital, Orlando, FL 32803, USA
| | - Jing Su
- Center for Bioinformatics & Systems Biology and Department of Radiology, Wake Forest School of Medicine, Winston-Salem, NC 27157, USA, Department of Radiology, The Methodist Hospital Research Institute, Houston, TX 77030, USA, Department of Pathology, The Methodist Hospital Research Institute, Houston, TX 77030, USA, Proteomics Programmatic Core Laboratory, The Methodist Hospital Research Institute, Houston, TX 77030, USA, College of Computer and Information Science, Southwest University, Chongqing 400715, China, Department of Pathology, Florida Hospital, Orlando, FL 32803, USA
| | - Le Zhang
- Center for Bioinformatics & Systems Biology and Department of Radiology, Wake Forest School of Medicine, Winston-Salem, NC 27157, USA, Department of Radiology, The Methodist Hospital Research Institute, Houston, TX 77030, USA, Department of Pathology, The Methodist Hospital Research Institute, Houston, TX 77030, USA, Proteomics Programmatic Core Laboratory, The Methodist Hospital Research Institute, Houston, TX 77030, USA, College of Computer and Information Science, Southwest University, Chongqing 400715, China, Department of Pathology, Florida Hospital, Orlando, FL 32803, USA
| | - Chung-Che Jeff Chang
- Center for Bioinformatics & Systems Biology and Department of Radiology, Wake Forest School of Medicine, Winston-Salem, NC 27157, USA, Department of Radiology, The Methodist Hospital Research Institute, Houston, TX 77030, USA, Department of Pathology, The Methodist Hospital Research Institute, Houston, TX 77030, USA, Proteomics Programmatic Core Laboratory, The Methodist Hospital Research Institute, Houston, TX 77030, USA, College of Computer and Information Science, Southwest University, Chongqing 400715, China, Department of Pathology, Florida Hospital, Orlando, FL 32803, USA
| | - Xiaobo Zhou
- Center for Bioinformatics & Systems Biology and Department of Radiology, Wake Forest School of Medicine, Winston-Salem, NC 27157, USA, Department of Radiology, The Methodist Hospital Research Institute, Houston, TX 77030, USA, Department of Pathology, The Methodist Hospital Research Institute, Houston, TX 77030, USA, Proteomics Programmatic Core Laboratory, The Methodist Hospital Research Institute, Houston, TX 77030, USA, College of Computer and Information Science, Southwest University, Chongqing 400715, China, Department of Pathology, Florida Hospital, Orlando, FL 32803, USACenter for Bioinformatics & Systems Biology and Department of Radiology, Wake Forest School of Medicine, Winston-Salem, NC 27157, USA, Department of Radiology, The Methodist Hospital Research Institute, Houston, TX 77030, USA, Department of Pathology, The Methodist Hospital Research Institute, Houston, TX 77030, USA, Proteomics Programmatic Core Laboratory, The Methodist Hospital Research Institute, Houston, TX 77030, USA, College of Computer and Information Science, Southwest University, Chongqing 400715, China, Department of Pathology, Florida Hospital, Orlando, FL 32803, USA
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Pang K, Wan YW, Choi WT, Donehower LA, Sun J, Pant D, Liu Z. Combinatorial therapy discovery using mixed integer linear programming. ACTA ACUST UNITED AC 2014; 30:1456-63. [PMID: 24463180 DOI: 10.1093/bioinformatics/btu046] [Citation(s) in RCA: 32] [Impact Index Per Article: 3.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/10/2023]
Abstract
MOTIVATION Combinatorial therapies play increasingly important roles in combating complex diseases. Owing to the huge cost associated with experimental methods in identifying optimal drug combinations, computational approaches can provide a guide to limit the search space and reduce cost. However, few computational approaches have been developed for this purpose, and thus there is a great need of new algorithms for drug combination prediction. RESULTS Here we proposed to formulate the optimal combinatorial therapy problem into two complementary mathematical algorithms, Balanced Target Set Cover (BTSC) and Minimum Off-Target Set Cover (MOTSC). Given a disease gene set, BTSC seeks a balanced solution that maximizes the coverage on the disease genes and minimizes the off-target hits at the same time. MOTSC seeks a full coverage on the disease gene set while minimizing the off-target set. Through simulation, both BTSC and MOTSC demonstrated a much faster running time over exhaustive search with the same accuracy. When applied to real disease gene sets, our algorithms not only identified known drug combinations, but also predicted novel drug combinations that are worth further testing. In addition, we developed a web-based tool to allow users to iteratively search for optimal drug combinations given a user-defined gene set. AVAILABILITY Our tool is freely available for noncommercial use at http://www.drug.liuzlab.org/. CONTACT zhandong.liu@bcm.edu SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online.
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Affiliation(s)
- Kaifang Pang
- Computational and Integrative Biomedical Research Center, Baylor College of Medicine, Jan and Dan Duncan Neurological Research Institute, Texas Children's Hospital, Department of Pediatrics-Neurology, Department of Obstetrics and Gynaecology, Department of Molecular Virology and Microbiology, Baylor College of Medicine, School of Biomedical Informatics, The University of Texas Health Science Center at Houston, Houston, TX 77030, USA, and Department of Cancer Biology, University of Pennsylvania, Philadelphia, PA 19104, USA
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Tang J, Aittokallio T. Network pharmacology strategies toward multi-target anticancer therapies: from computational models to experimental design principles. Curr Pharm Des 2014; 20:23-36. [PMID: 23530504 PMCID: PMC3894695 DOI: 10.2174/13816128113199990470] [Citation(s) in RCA: 84] [Impact Index Per Article: 8.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/06/2013] [Accepted: 03/18/2013] [Indexed: 12/12/2022]
Abstract
Polypharmacology has emerged as novel means in drug discovery for improving treatment response in clinical use. However, to really capitalize on the polypharmacological effects of drugs, there is a critical need to better model and understand how the complex interactions between drugs and their cellular targets contribute to drug efficacy and possible side effects. Network graphs provide a convenient modeling framework for dealing with the fact that most drugs act on cellular systems through targeting multiple proteins both through on-target and off-target binding. Network pharmacology models aim at addressing questions such as how and where in the disease network should one target to inhibit disease phenotypes, such as cancer growth, ideally leading to therapies that are less vulnerable to drug resistance and side effects by means of attacking the disease network at the systems level through synergistic and synthetic lethal interactions. Since the exponentially increasing number of potential drug target combinations makes pure experimental approach quickly unfeasible, this review depicts a number of computational models and algorithms that can effectively reduce the search space for determining the most promising combinations for experimental evaluation. Such computational-experimental strategies are geared toward realizing the full potential of multi-target treatments in different disease phenotypes. Our specific focus is on system-level network approaches to polypharmacology designs in anticancer drug discovery, where we give representative examples of how network-centric modeling may offer systematic strategies toward better understanding and even predicting the phenotypic responses to multi-target therapies.
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34
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Mijung Park, Nassar M, Vikalo H. Bayesian Active Learning for Drug Combinations. IEEE Trans Biomed Eng 2013; 60:3248-55. [DOI: 10.1109/tbme.2013.2272322] [Citation(s) in RCA: 13] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/10/2022]
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35
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Sun X, Vilar S, Tatonetti NP. High-Throughput Methods for Combinatorial Drug Discovery. Sci Transl Med 2013; 5:205rv1. [DOI: 10.1126/scitranslmed.3006667] [Citation(s) in RCA: 115] [Impact Index Per Article: 10.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/20/2023]
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Csermely P, Korcsmáros T, Kiss HJM, London G, Nussinov R. Structure and dynamics of molecular networks: a novel paradigm of drug discovery: a comprehensive review. Pharmacol Ther 2013; 138:333-408. [PMID: 23384594 PMCID: PMC3647006 DOI: 10.1016/j.pharmthera.2013.01.016] [Citation(s) in RCA: 511] [Impact Index Per Article: 46.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/22/2013] [Accepted: 01/22/2013] [Indexed: 02/02/2023]
Abstract
Despite considerable progress in genome- and proteome-based high-throughput screening methods and in rational drug design, the increase in approved drugs in the past decade did not match the increase of drug development costs. Network description and analysis not only give a systems-level understanding of drug action and disease complexity, but can also help to improve the efficiency of drug design. We give a comprehensive assessment of the analytical tools of network topology and dynamics. The state-of-the-art use of chemical similarity, protein structure, protein-protein interaction, signaling, genetic interaction and metabolic networks in the discovery of drug targets is summarized. We propose that network targeting follows two basic strategies. The "central hit strategy" selectively targets central nodes/edges of the flexible networks of infectious agents or cancer cells to kill them. The "network influence strategy" works against other diseases, where an efficient reconfiguration of rigid networks needs to be achieved by targeting the neighbors of central nodes/edges. It is shown how network techniques can help in the identification of single-target, edgetic, multi-target and allo-network drug target candidates. We review the recent boom in network methods helping hit identification, lead selection optimizing drug efficacy, as well as minimizing side-effects and drug toxicity. Successful network-based drug development strategies are shown through the examples of infections, cancer, metabolic diseases, neurodegenerative diseases and aging. Summarizing >1200 references we suggest an optimized protocol of network-aided drug development, and provide a list of systems-level hallmarks of drug quality. Finally, we highlight network-related drug development trends helping to achieve these hallmarks by a cohesive, global approach.
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Affiliation(s)
- Peter Csermely
- Department of Medical Chemistry, Semmelweis University, P.O. Box 260, H-1444 Budapest 8, Hungary.
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37
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Wu Z, Wang Y, Chen L. Network-based drug repositioning. MOLECULAR BIOSYSTEMS 2013; 9:1268-81. [PMID: 23493874 DOI: 10.1039/c3mb25382a] [Citation(s) in RCA: 100] [Impact Index Per Article: 9.1] [Reference Citation Analysis] [Abstract] [Subscribe] [Scholar Register] [Indexed: 12/23/2022]
Abstract
Network-based computational biology, with the emphasis on biomolecular interactions and omics-data integration, has had success in drug development and created new directions such as drug repositioning and drug combination. Drug repositioning, i.e., revealing a drug's new roles, is increasingly attracting much attention from the pharmaceutical community to tackle the problems of high failure rate and long-term development in drug discovery. While drug combination or drug cocktails, i.e., combining multiple drugs against diseases, mainly aims to alleviate the problems of the recurrent emergence of drug resistance and also reveal their synergistic effects. In this paper, we unify the two topics to reveal new roles of drug interactions from a network perspective by treating drug combination as another form of drug repositioning. In particular, first, we emphasize that rationally repositioning drugs in the large scale is driven by the accumulation of various high-throughput genome-wide data. These data can be utilized to capture the interplay among targets and biological molecules, uncover the resulting network structures, and further bridge molecular profiles and phenotypes. This motivates many network-based computational methods on these topics. Second, we organize these existing methods into two categories, i.e., single drug repositioning and drug combination, and further depict their main features by three data sources. Finally, we discuss the merits and shortcomings of these methods and pinpoint some future topics in this promising field.
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Affiliation(s)
- Zikai Wu
- Business School, University of Shanghai for Science and Technology, Shanghai, China
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38
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Predicting in silico which mixtures of the natural products of plants might most effectively kill human leukemia cells? EVIDENCE-BASED COMPLEMENTARY AND ALTERNATIVE MEDICINE 2013; 2013:801501. [PMID: 23431350 PMCID: PMC3569894 DOI: 10.1155/2013/801501] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 09/04/2012] [Revised: 12/06/2012] [Accepted: 12/10/2012] [Indexed: 11/18/2022]
Abstract
The aim of the analysis of just 13 natural products of plants was to predict the most likely effective artificial mixtures of 2-3 most effective natural products on leukemia cells from over 364 possible mixtures. The natural product selected included resveratrol, honokiol, chrysin, limonene, cholecalciferol, cerulenin, aloe emodin, and salicin and had over 600 potential protein targets. Target profiling used the Ontomine set of tools for literature searches of potential binding proteins, binding constant predictions, binding site predictions, and pathway network pattern analysis. The analyses indicated that 6 of the 13 natural products predicted binding proteins which were important targets for established cancer treatments. Improvements in effectiveness were predicted for artificial combinations of 2 or 3 natural products. That effect might be attributed to drug synergism rather than increased numbers of binding proteins bound (dose effects). Among natural products, the combinations of aloe emodin with mevinolin and honokiol were predicted to be the most effective combination for AML-related predicted binding proteins. Therefore, plant extracts may in future provide more effective medicines than the single purified natural products of modern medicine, in some cases.
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Kanwal MS, Ramesh AS, Huang LA. A novel pseudoderivative-based mutation operator for real-coded adaptive genetic algorithms. F1000Res 2013; 2:139. [PMID: 24627784 PMCID: PMC3924953 DOI: 10.12688/f1000research.2-139.v2] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Accepted: 09/23/2013] [Indexed: 11/20/2022] Open
Abstract
Recent development of large databases, especially those in genetics and proteomics, is pushing the development of novel computational algorithms that implement rapid and accurate search strategies. One successful approach has been to use artificial intelligence and methods, including pattern recognition (e.g. neural networks) and optimization techniques (e.g. genetic algorithms). The focus of this paper is on optimizing the design of genetic algorithms by using an adaptive mutation rate that is derived from comparing the fitness values of successive generations. We propose a novel pseudoderivative-based mutation rate operator designed to allow a genetic algorithm to escape local optima and successfully continue to the global optimum. Once proven successful, this algorithm can be implemented to solve real problems in neurology and bioinformatics. As a first step towards this goal, we tested our algorithm on two 3-dimensional surfaces with multiple local optima, but only one global optimum, as well as on the N-queens problem, an applied problem in which the function that maps the curve is implicit. For all tests, the adaptive mutation rate allowed the genetic algorithm to find the global optimal solution, performing significantly better than other search methods, including genetic algorithms that implement fixed mutation rates.
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Affiliation(s)
- Maxinder S Kanwal
- Thomas Jefferson High School for Science and Technology, Alexandria, VA 22312, USA
| | - Avinash S Ramesh
- Thomas Jefferson High School for Science and Technology, Alexandria, VA 22312, USA
| | - Lauren A Huang
- Thomas Jefferson High School for Science and Technology, Alexandria, VA 22312, USA
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Kim M, Yoon BJ. Adaptive reference update (ARU) algorithm. A stochastic search algorithm for efficient optimization of multi-drug cocktails. BMC Genomics 2012; 13 Suppl 6:S12. [PMID: 23134742 PMCID: PMC3488974 DOI: 10.1186/1471-2164-13-s6-s12] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/10/2022] Open
Abstract
Background Multi-target therapeutics has been shown to be effective for treating complex diseases, and currently, it is a common practice to combine multiple drugs to treat such diseases to optimize the therapeutic outcomes. However, considering the huge number of possible ways to mix multiple drugs at different concentrations, it is practically difficult to identify the optimal drug combination through exhaustive testing. Results In this paper, we propose a novel stochastic search algorithm, called the adaptive reference update (ARU) algorithm, that can provide an efficient and systematic way for optimizing multi-drug cocktails. The ARU algorithm iteratively updates the drug combination to improve its response, where the update is made by comparing the response of the current combination with that of a reference combination, based on which the beneficial update direction is predicted. The reference combination is continuously updated based on the drug response values observed in the past, thereby adapting to the underlying drug response function. To demonstrate the effectiveness of the proposed algorithm, we evaluated its performance based on various multi-dimensional drug functions and compared it with existing algorithms. Conclusions Simulation results show that the ARU algorithm significantly outperforms existing stochastic search algorithms, including the Gur Game algorithm. In fact, the ARU algorithm can more effectively identify potent drug combinations and it typically spends fewer iterations for finding effective combinations. Furthermore, the ARU algorithm is robust to random fluctuations and noise in the measured drug response, which makes the algorithm well-suited for practical drug optimization applications.
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Affiliation(s)
- Mansuck Kim
- Department of Electrical and Computer Engineering, Texas A&M University, College Station, TX 77843-3128, USA
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Seyres D, Röder L, Perrin L. Genes and networks regulating cardiac development and function in flies: genetic and functional genomic approaches. Brief Funct Genomics 2012; 11:366-74. [PMID: 22908209 DOI: 10.1093/bfgp/els028] [Citation(s) in RCA: 13] [Impact Index Per Article: 1.1] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/20/2022] Open
Abstract
The Drosophila heart has emerged as a powerful model system for cardiovascular research. This simple organ, composed of only 104 cardiomyocytes and associated pericardiac cells, has been the focus of numerous candidate gene approaches in the last 2 decades, which have unraveled a number of transcription factors and signaling pathways involved in the regulation of early cardiac development. Importantly, these regulators seem to have largely conserved functions in mammals. Recent studies also demonstrated the usefulness of the fly circulatory system to investigate molecular mechanisms involved in the control of the establishment and maintenance of the cardiac function. In this review, we have focused on how new technological and conceptual advances in the field of functional genomics have impacted research on the cardiovascular system in Drosophila. Genome-scale genetic screens were conducted taking advantage of recently developed ribonucleic acid interference transgenic lines and molecularly defined genetic deficiencies, which have provided new insights into the genetics of both the developmental control of heart formation and cardiac function. In addition, a comprehensive picture of the transcriptional network controlling heart formation is emerging, thanks to newly developed genomic approaches which allow global and unbiased identification of the underlying components of gene regulatory circuits.
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Affiliation(s)
- Denis Seyres
- Life and Health Science Doctoral Program, Université d' Aix-Marseille, Marseille, France
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Huang X, Chen DYK. A Case Study of Single-Pill Combination Therapy: The Ezetimibe/Simvastatin Combination for Treatment of Hyperlipidemia. ChemMedChem 2012; 7:1882-94. [DOI: 10.1002/cmdc.201200287] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/07/2012] [Indexed: 12/11/2022]
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Abstract
Background Combination of different agents is widely used in clinic to combat complex diseases with improved therapy and reduced side effects. However, the identification of effective drug combinations remains a challenging task due to the huge number of possible combinations among candidate drugs that makes it impractical to screen putative combinations. Results In this work, we construct a 'drug cocktail network' using all the known effective drug combinations extracted from the Drug Combination Database (DCDB), and propose a network-based approach to investigate drug combinations. Our results show that the agents in an effective combination tend to have more similar therapeutic effects and share more interaction partners. Based on our observations, we further develop a statistical approach termed as DCPred (Drug Combination Predictor) to predict possible drug combinations by exploiting the topological features of the drug cocktail network. Validating on the known drug combinations, DCPred achieves the overall AUC (Area Under the receiver operating characteristic Curve) score of 0.92, indicating the predictive power of our proposed approach. Conclusions The drug cocktail network constructed in this work provides useful insights into the underlying rules of effective drug combinations and offer important clues to accelerate the future discovery of new drug combinations.
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Affiliation(s)
- Ke-Jia Xu
- Institute of Systems Biology, Shanghai University, Shanghai, China.
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44
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Wang YY, Xu KJ, Song J, Zhao XM. Exploring drug combinations in genetic interaction network. BMC Bioinformatics 2012; 13 Suppl 7:S7. [PMID: 22595004 PMCID: PMC3348050 DOI: 10.1186/1471-2105-13-s7-s7] [Citation(s) in RCA: 26] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/02/2022] Open
Abstract
Background Drug combination that consists of distinctive agents is an attractive strategy to combat complex diseases and has been widely used clinically with improved therapeutic effects. However, the identification of efficacious drug combinations remains a non-trivial and challenging task due to the huge number of possible combinations among the candidate drugs. As an important factor, the molecular context in which drugs exert their functions can provide crucial insights into the mechanism underlying drug combinations. Results In this work, we present a network biology approach to investigate drug combinations and their target proteins in the context of genetic interaction networks and the related human pathways, in order to better understand the underlying rules of effective drug combinations. Our results indicate that combinatorial drugs tend to have a smaller effect radius in the genetic interaction networks, which is an important parameter to describe the therapeutic effect of a drug combination from the network perspective. We also find that drug combinations are more likely to modulate functionally related pathways. Conclusions This study confirms that the molecular networks where drug combinations exert their functions can indeed provide important insights into the underlying rules of effective drug combinations. We hope that our findings can help shortcut the expedition of the future discovery of novel drug combinations.
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Affiliation(s)
- Yin-Ying Wang
- Institute of Systems Biology, Shanghai University, Shanghai 200444, China
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45
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Dasatinib synergizes with both cytotoxic and signal transduction inhibitors in heterogeneous breast cancer cell lines--lessons for design of combination targeted therapy. Cancer Lett 2012; 320:104-10. [PMID: 22306341 DOI: 10.1016/j.canlet.2012.01.039] [Citation(s) in RCA: 34] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/11/2012] [Revised: 01/26/2012] [Accepted: 01/26/2012] [Indexed: 11/21/2022]
Abstract
Molecularly targeted therapies have emerged as the leading theme in cancer therapeutics. Multi-cytotoxic drug regimens have been highly successful, yet many studies in targeted therapeutics have centered on a single agent. We investigated whether the Src/Abl kinase inhibitor dasatinib displays synergy with other agents in molecularly heterogeneous breast cancer cell lines. MCF-7, SKBR-3, and MDA-MB-231 display different signaling and gene signatures profiles due to expression of the estrogen receptor, ErbB2, or neither. Cell proliferation was measured following treatment with dasatinib±cytotoxic (paclitaxel, ixabepilone) or molecularly targeted agents (tamoxifen, rapamycin, sorafenib, pan PI3K inhibitor LY294002, and MEK/ERK inhibitor U0126). Dose-responses for single or combination drugs were calculated and analyzed by the Chou-Talalay method. The drugs with the greatest level of synergy with dasatinib were rapamycin, ixabepilone, and sorafenib, for the MDA-MB-231, MCF-7, and SK-BR-3 cell lines respectively. However, dasatinib synergized with both cytotoxic and molecularly targeted agents in all three molecularly heterogeneous breast cancer cell lines. These results suggest that effectiveness of rationally designed therapies may not entirely rest on precise identification of gene signatures or molecular profiling. Since a systems analysis that reveals emergent properties cannot be easily performed for each cancer case, multi-drug regimens in the near future will still involve empirical design.
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46
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Feala JD, Cortes J, Duxbury PM, McCulloch AD, Piermarocchi C, Paternostro G. Statistical properties and robustness of biological controller-target networks. PLoS One 2012; 7:e29374. [PMID: 22235289 PMCID: PMC3250441 DOI: 10.1371/journal.pone.0029374] [Citation(s) in RCA: 11] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/03/2011] [Accepted: 11/28/2011] [Indexed: 01/04/2023] Open
Abstract
Cells are regulated by networks of controllers having many targets, and targets affected by many controllers, in a “many-to-many” control structure. Here we study several of these bipartite (two-layer) networks. We analyze both naturally occurring biological networks (composed of transcription factors controlling genes, microRNAs controlling mRNA transcripts, and protein kinases controlling protein substrates) and a drug-target network composed of kinase inhibitors and of their kinase targets. Certain statistical properties of these biological bipartite structures seem universal across systems and species, suggesting the existence of common control strategies in biology. The number of controllers is ∼8% of targets and the density of links is 2.5%±1.2%. Links per node are predominantly exponentially distributed. We explain the conservation of the mean number of incoming links per target using a mathematical model of control networks, which also indicates that the “many-to-many” structure of biological control has properties of efficient robustness. The drug-target network has many statistical properties similar to the biological networks and we show that drug-target networks with biomimetic features can be obtained. These findings suggest a completely new approach to pharmacological control of biological systems. Molecular tools, such as kinase inhibitors, are now available to test if therapeutic combinations may benefit from being designed with biomimetic properties, such as “many-to-many” targeting, very wide coverage of the target set, and redundancy of incoming links per target.
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Affiliation(s)
- Jacob D. Feala
- Sanford-Burnham Medical Research Institute, La Jolla, California, United States of America
| | - Jorge Cortes
- Department of Mechanical and Aerospace Engineering, University of California San Diego, La Jolla, California, United States of America
| | - Phillip M. Duxbury
- Department of Physics and Astronomy, Michigan State University, East Lansing, Michigan, United States of America
| | - Andrew D. McCulloch
- Department of Bioengineering, University of California San Diego, La Jolla, California, United States of America
| | - Carlo Piermarocchi
- Department of Physics and Astronomy, Michigan State University, East Lansing, Michigan, United States of America
| | - Giovanni Paternostro
- Sanford-Burnham Medical Research Institute, La Jolla, California, United States of America
- * E-mail:
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47
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Tiziani S, Kang Y, Choi JS, Roberts W, Paternostro G. Metabolomic high-content nuclear magnetic resonance-based drug screening of a kinase inhibitor library. Nat Commun 2011; 2:545. [PMID: 22109519 DOI: 10.1038/ncomms1562] [Citation(s) in RCA: 44] [Impact Index Per Article: 3.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/23/2011] [Accepted: 10/21/2011] [Indexed: 01/13/2023] Open
Abstract
Metabolism is altered in many highly prevalent diseases and is controlled by a complex network of intracellular regulators. Monitoring cell metabolism during treatment is extremely valuable to investigate cellular response and treatment efficacy. Here we describe a nuclear magnetic resonance-based method for screening of the metabolomic response of drug-treated mammalian cells in a 96-well format. We validate the method using drugs having well-characterized targets and report the results of a screen of a kinase inhibitor library. Four hits are validated from their action on an important clinical parameter, the lactate to pyruvate ratio. An eEF-2 kinase inhibitor and an NF-kB activation inhibitor increased lactate/pyruvate ratio, whereas an MK2 inhibitor and an inhibitor of PKA, PKC and PKG induced a decrease. The method is validated in cell lines and in primary cancer cells, and may have potential applications in both drug development and personalized therapy.
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Affiliation(s)
- Stefano Tiziani
- Sanford-Burnham Medical Research Institute, 10901 North Torrey Pines Road, La Jolla, California 92037, USA
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48
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Xie L, Xie L, Kinnings SL, Bourne PE. Novel computational approaches to polypharmacology as a means to define responses to individual drugs. Annu Rev Pharmacol Toxicol 2011; 52:361-79. [PMID: 22017683 DOI: 10.1146/annurev-pharmtox-010611-134630] [Citation(s) in RCA: 150] [Impact Index Per Article: 11.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/09/2022]
Abstract
Polypharmacology, which focuses on designing therapeutics to target multiple receptors, has emerged as a new paradigm in drug discovery. Polypharmacological effects are an attribute of most, if not all, drug molecules. The efficacy and toxicity of drugs, whether designed as single- or multitarget therapeutics, result from complex interactions between pharmacodynamic, pharmacokinetic, genetic, epigenetic, and environmental factors. Ultimately, to predict a drug response phenotype, it is necessary to understand the change in information flow through cellular networks resulting from dynamic drug-target interactions and the impact that this has on the complete biological system. Although such is a future objective, we review recent progress and challenges in computational techniques that enable the prediction and analysis of in vitro and in vivo drug-response phenotypes.
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Affiliation(s)
- Lei Xie
- Department of Computer Science, Hunter College, The City University of New York, New York, New York 10065, USA.
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49
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Yu F, Al-Shyoukh I, Feng J, Li X, Liao CW, Ho CM, Shamma JS, Sun R. Control of Kaposi's sarcoma-associated herpesvirus reactivation induced by multiple signals. PLoS One 2011; 6:e20998. [PMID: 21904595 PMCID: PMC3125160 DOI: 10.1371/journal.pone.0020998] [Citation(s) in RCA: 9] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/08/2011] [Accepted: 05/16/2011] [Indexed: 11/18/2022] Open
Abstract
The ability to control cellular functions can bring about many developments in basic biological research and its applications. The presence of multiple signals, internal as well as externally imposed, introduces several challenges for controlling cellular functions. Additionally the lack of clear understanding of the cellular signaling network limits our ability to infer the responses to a number of signals. This work investigates the control of Kaposi's sarcoma-associated herpesvirus reactivation upon treatment with a combination of multiple signals. We utilize mathematical model-based as well as experiment-based approaches to achieve the desired goals of maximizing virus reactivation. The results show that appropriately selected control signals can induce virus lytic gene expression about ten folds higher than a single drug; these results were validated by comparing the results of the two approaches, and experimentally using multiple assays. Additionally, we have quantitatively analyzed potential interactions between the used combinations of drugs. Some of these interactions were consistent with existing literature, and new interactions emerged and warrant further studies. The work presents a general method that can be used to quantitatively and systematically study multi-signal induced responses. It enables optimization of combinations to achieve desired responses. It also allows identifying critical nodes mediating the multi-signal induced responses. The concept and the approach used in this work will be directly applicable to other diseases such as AIDS and cancer.
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Affiliation(s)
- Fuqu Yu
- Department of Molecular and Medical Pharmacology, University of California Los Angeles, Los Angeles, California, United States of America
| | - Ibrahim Al-Shyoukh
- Department of Molecular and Medical Pharmacology, University of California Los Angeles, Los Angeles, California, United States of America
- Mechanical and Aerospace Engineering Department, University of California Los Angeles, Los Angeles, California, United States of America
| | - Jiaying Feng
- Department of Molecular and Medical Pharmacology, University of California Los Angeles, Los Angeles, California, United States of America
| | - Xudong Li
- Department of Molecular and Medical Pharmacology, University of California Los Angeles, Los Angeles, California, United States of America
| | - Chia Wei Liao
- Department of Molecular and Medical Pharmacology, University of California Los Angeles, Los Angeles, California, United States of America
| | - Chih-Ming Ho
- Mechanical and Aerospace Engineering Department, University of California Los Angeles, Los Angeles, California, United States of America
| | - Jeff S. Shamma
- School of Electrical and Computer Engineering, Georgia Institute of Technology, Atlanta, Georgia, United States of America
| | - Ren Sun
- Department of Molecular and Medical Pharmacology, University of California Los Angeles, Los Angeles, California, United States of America
- * E-mail:
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50
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Al-Shyoukh I, Yu F, Feng J, Yan K, Dubinett S, Ho CM, Shamma JS, Sun R. Systematic quantitative characterization of cellular responses induced by multiple signals. BMC SYSTEMS BIOLOGY 2011; 5:88. [PMID: 21624115 PMCID: PMC3138445 DOI: 10.1186/1752-0509-5-88] [Citation(s) in RCA: 51] [Impact Index Per Article: 3.9] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 04/20/2010] [Accepted: 05/30/2011] [Indexed: 11/15/2022]
Abstract
Background Cells constantly sense many internal and environmental signals and respond through their complex signaling network, leading to particular biological outcomes. However, a systematic characterization and optimization of multi-signal responses remains a pressing challenge to traditional experimental approaches due to the arising complexity associated with the increasing number of signals and their intensities. Results We established and validated a data-driven mathematical approach to systematically characterize signal-response relationships. Our results demonstrate how mathematical learning algorithms can enable systematic characterization of multi-signal induced biological activities. The proposed approach enables identification of input combinations that can result in desired biological responses. In retrospect, the results show that, unlike a single drug, a properly chosen combination of drugs can lead to a significant difference in the responses of different cell types, increasing the differential targeting of certain combinations. The successful validation of identified combinations demonstrates the power of this approach. Moreover, the approach enables examining the efficacy of all lower order mixtures of the tested signals. The approach also enables identification of system-level signaling interactions between the applied signals. Many of the signaling interactions identified were consistent with the literature, and other unknown interactions emerged. Conclusions This approach can facilitate development of systems biology and optimal drug combination therapies for cancer and other diseases and for understanding key interactions within the cellular network upon treatment with multiple signals.
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Affiliation(s)
- Ibrahim Al-Shyoukh
- Department of Molecular and Medical Pharmacology, University of California at Los Angeles, Los Angeles, CA 90095, USA
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