1
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Smith DA, Burton LM, Smith SA. Through a computer monitor darkly: artificial intelligence in absorption, distribution, metabolism and excretion science. Xenobiotica 2024; 54:359-367. [PMID: 38095217 DOI: 10.1080/00498254.2023.2295361] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/06/2023] [Accepted: 12/12/2023] [Indexed: 08/22/2024]
Abstract
Artificial Intelligence (AI) is poised or has already begun to influence absorption, distribution, metabolism and excretion (ADME) science. It is not in the area expected - that of superior modelling of ADME data to increase its predictive power. It is influencing traditional exhaustive and careful literature research by providing almost perfect summaries of existing information. This will highly influence how people study, graduate and progress in the ADME sciences. The literature contains many flaws (protein binding influence on unbound drug concentration is one of the examples cited) and without direction AI may help to popularise them.ADME science has a relatively small number of key assays and values, but these are produced under widely varying conditions so large data sets, the best substrate for artificial intelligence, are not readily available to produce new more predictive systems. The use of AI to enrich the databases may be a near term goal.AI is already contributing in other areas such as technical skill assimilation, maintenance of complex instruments (combined with virtual reality) and the processing of pharmacovigilance.
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2
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Rath M, Wellnitz J, Martin HJ, Melo-Filho C, Hochuli JE, Silva GM, Beasley JM, Travis M, Sessions ZL, Popov KI, Zakharov AV, Cherkasov A, Alves V, Muratov EN, Tropsha A. Pharmacokinetics Profiler (PhaKinPro): Model Development, Validation, and Implementation as a Web Tool for Triaging Compounds with Undesired Pharmacokinetics Profiles. J Med Chem 2024; 67:6508-6518. [PMID: 38568752 DOI: 10.1021/acs.jmedchem.3c02446] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 04/05/2024]
Abstract
Computational models that predict pharmacokinetic properties are critical to deprioritize drug candidates that emerge as hits in high-throughput screening campaigns. We collected, curated, and integrated a database of compounds tested in 12 major end points comprising over 10,000 unique molecules. We then employed these data to build and validate binary quantitative structure-activity relationship (QSAR) models. All trained models achieved a correct classification rate above 0.60 and a positive predictive value above 0.50. To illustrate their utility in drug discovery, we used these models to predict the pharmacokinetic properties for drugs in the NCATS Inxight Drugs database. In addition, we employed the developed models to predict the pharmacokinetic properties of all compounds in the DrugBank. All models described in this paper have been integrated and made publicly available via the PhaKinPro Web-portal that can be accessed at https://phakinpro.mml.unc.edu/.
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Affiliation(s)
- Marielle Rath
- Laboratory for Molecular Modeling, Division of Chemical Biology and Medicinal Chemistry, UNC Eshelman School of Pharmacy, University of North Carolina, Chapel Hill, North Carolina 27599, United States
| | - James Wellnitz
- Laboratory for Molecular Modeling, Division of Chemical Biology and Medicinal Chemistry, UNC Eshelman School of Pharmacy, University of North Carolina, Chapel Hill, North Carolina 27599, United States
| | - Holli-Joi Martin
- Laboratory for Molecular Modeling, Division of Chemical Biology and Medicinal Chemistry, UNC Eshelman School of Pharmacy, University of North Carolina, Chapel Hill, North Carolina 27599, United States
| | - Cleber Melo-Filho
- Laboratory for Molecular Modeling, Division of Chemical Biology and Medicinal Chemistry, UNC Eshelman School of Pharmacy, University of North Carolina, Chapel Hill, North Carolina 27599, United States
| | - Joshua E Hochuli
- Laboratory for Molecular Modeling, Division of Chemical Biology and Medicinal Chemistry, UNC Eshelman School of Pharmacy, University of North Carolina, Chapel Hill, North Carolina 27599, United States
| | - Guilherme Martins Silva
- Laboratory for Molecular Modeling, Division of Chemical Biology and Medicinal Chemistry, UNC Eshelman School of Pharmacy, University of North Carolina, Chapel Hill, North Carolina 27599, United States
| | - Jon-Michael Beasley
- Laboratory for Molecular Modeling, Division of Chemical Biology and Medicinal Chemistry, UNC Eshelman School of Pharmacy, University of North Carolina, Chapel Hill, North Carolina 27599, United States
| | - Maxfield Travis
- Laboratory for Molecular Modeling, Division of Chemical Biology and Medicinal Chemistry, UNC Eshelman School of Pharmacy, University of North Carolina, Chapel Hill, North Carolina 27599, United States
| | - Zoe L Sessions
- Laboratory for Molecular Modeling, Division of Chemical Biology and Medicinal Chemistry, UNC Eshelman School of Pharmacy, University of North Carolina, Chapel Hill, North Carolina 27599, United States
| | - Konstantin I Popov
- Laboratory for Molecular Modeling, Division of Chemical Biology and Medicinal Chemistry, UNC Eshelman School of Pharmacy, University of North Carolina, Chapel Hill, North Carolina 27599, United States
| | - Alexey V Zakharov
- National Center for Advancing Translational Sciences (NCATS), National Institutes of Health, 9800 Medical Center Drive, Rockville, Maryland 20850, United States
| | - Artem Cherkasov
- Vancouver Prostate Centre, University of British Columbia, Vancouver, British Columbia V6H3Z6, Canada
| | - Vinicius Alves
- Laboratory for Molecular Modeling, Division of Chemical Biology and Medicinal Chemistry, UNC Eshelman School of Pharmacy, University of North Carolina, Chapel Hill, North Carolina 27599, United States
| | - Eugene N Muratov
- Laboratory for Molecular Modeling, Division of Chemical Biology and Medicinal Chemistry, UNC Eshelman School of Pharmacy, University of North Carolina, Chapel Hill, North Carolina 27599, United States
| | - Alexander Tropsha
- Laboratory for Molecular Modeling, Division of Chemical Biology and Medicinal Chemistry, UNC Eshelman School of Pharmacy, University of North Carolina, Chapel Hill, North Carolina 27599, United States
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3
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Fralish Z, Chen A, Skaluba P, Reker D. DeepDelta: predicting ADMET improvements of molecular derivatives with deep learning. J Cheminform 2023; 15:101. [PMID: 37885017 PMCID: PMC10605784 DOI: 10.1186/s13321-023-00769-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/23/2023] [Accepted: 10/12/2023] [Indexed: 10/28/2023] Open
Abstract
Established molecular machine learning models process individual molecules as inputs to predict their biological, chemical, or physical properties. However, such algorithms require large datasets and have not been optimized to predict property differences between molecules, limiting their ability to learn from smaller datasets and to directly compare the anticipated properties of two molecules. Many drug and material development tasks would benefit from an algorithm that can directly compare two molecules to guide molecular optimization and prioritization, especially for tasks with limited available data. Here, we develop DeepDelta, a pairwise deep learning approach that processes two molecules simultaneously and learns to predict property differences between two molecules from small datasets. On 10 ADMET benchmark tasks, our DeepDelta approach significantly outperforms two established molecular machine learning algorithms, the directed message passing neural network (D-MPNN) ChemProp and Random Forest using radial fingerprints, for 70% of benchmarks in terms of Pearson's r, 60% of benchmarks in terms of mean absolute error (MAE), and all external test sets for both Pearson's r and MAE. We further analyze our performance and find that DeepDelta is particularly outperforming established approaches at predicting large differences in molecular properties and can perform scaffold hopping. Furthermore, we derive mathematically fundamental computational tests of our models based on mathematical invariants and show that compliance to these tests correlates with overall model performance - providing an innovative, unsupervised, and easily computable measure of expected model performance and applicability. Taken together, DeepDelta provides an accurate approach to predict molecular property differences by directly training on molecular pairs and their property differences to further support fidelity and transparency in molecular optimization for drug development and the chemical sciences.
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Affiliation(s)
- Zachary Fralish
- Department of Biomedical Engineering, Duke University, Durham, NC, 27708, USA
| | - Ashley Chen
- Department of Computer Science, Duke University, Durham, NC, 27708, USA
| | - Paul Skaluba
- Department of Biomedical Engineering, Duke University, Durham, NC, 27708, USA
| | - Daniel Reker
- Department of Biomedical Engineering, Duke University, Durham, NC, 27708, USA.
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4
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Gomatam A, Joseph B, Advani P, Shaikh M, Iyer K, Coutinho E. How effective are ionization state-based QSPKR models at predicting pharmacokinetic parameters in humans? Mol Divers 2023; 27:1675-1687. [PMID: 36219381 DOI: 10.1007/s11030-022-10520-7] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/20/2022] [Accepted: 08/24/2022] [Indexed: 11/25/2022]
Abstract
Optimizing the pharmacokinetics (PK) of a drug candidate to support oral dosing is a key challenge in drug development. PK parameters are usually estimated from the concentration-time profile following intravenous administration; however, traditional methods are time-consuming and expensive. In recent years, quantitative structure-pharmacokinetic relationship (QSPKR), an in silico tool that aims to develop a mathematical relationship between the structure of a molecule and its PK properties, has emerged as a useful alternative to experimental testing. Due to the complex nature of the various processes involved in dictating the fate of a drug, the development of adequate QSPKR models that can be used in real-world pre-screening situations has proved challenging. Given the crucial role played by a molecule's ionization state in determining its PK properties, this work aims to build predictive QSPKR models for PK parameters in humans using an ionization state-based strategy. We divide a high-quality dataset into clusters based on ionization state at physiological pH and build global and ion subset-based 'local' models for three major PK parameters: plasma clearance (CL), steady-state volume of distribution (VDss), and half-life (t1/2). We use a robust methodology developed in our lab entitled 'EigenValue ANalySis' that accounts for the stereospecificity in drug disposition and use the support vector machine algorithm for model building. Our findings suggest that categorizing compounds in accordance with ionization state does not result in improved QSPKR models. The narrow ranges in the endpoints along with redundancies in the data adversely affect the ion subset-based QSPKR models. We suggest alternative approaches such as elimination route-based models that account for drug-transporter interactions for CL and chemotype-specific QSPKR for VDss.
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Affiliation(s)
- Anish Gomatam
- Department of Pharmaceutical Chemistry, Bombay College of Pharmacy, Mumbai, India
| | - Blessy Joseph
- Department of Pharmaceutical Chemistry, Bombay College of Pharmacy, Mumbai, India
| | - Poonam Advani
- Mumbai Educational Trust's Institute of Pharmacy, Mumbai, India
| | - Mushtaque Shaikh
- Department of Pharmaceutical Chemistry, Vivekanand Education Society's College of Pharmacy, Mumbai, India
| | - Krishna Iyer
- Department of Pharmaceutical Chemistry, Bombay College of Pharmacy, Mumbai, India
| | - Evans Coutinho
- Department of Pharmaceutical Chemistry, Bombay College of Pharmacy, Mumbai, India.
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5
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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: 27] [Impact Index Per Article: 27.0] [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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6
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Recent Studies of Artificial Intelligence on In Silico Drug Distribution Prediction. Int J Mol Sci 2023; 24:ijms24031815. [PMID: 36768139 PMCID: PMC9915725 DOI: 10.3390/ijms24031815] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/14/2022] [Revised: 01/11/2023] [Accepted: 01/13/2023] [Indexed: 01/19/2023] Open
Abstract
Drug distribution is an important process in pharmacokinetics because it has the potential to influence both the amount of medicine reaching the active sites and the effectiveness as well as safety of the drug. The main causes of 90% of drug failures in clinical development are lack of efficacy and uncontrolled toxicity. In recent years, several advances and promising developments in drug distribution property prediction have been achieved, especially in silico, which helped to drastically reduce the time and expense of screening undesired drug candidates. In this study, we provide comprehensive knowledge of drug distribution background, influencing factors, and artificial intelligence-based distribution property prediction models from 2019 to the present. Additionally, we gathered and analyzed public databases and datasets commonly utilized by the scientific community for distribution prediction. The distribution property prediction performance of five large ADMET prediction tools is mentioned as a benchmark for future research. On this basis, we also offer future challenges in drug distribution prediction and research directions. We hope that this review will provide researchers with helpful insight into distribution prediction, thus facilitating the development of innovative approaches for drug discovery.
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7
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Aldeghi M, Graff DE, Frey N, Morrone JA, Pyzer-Knapp EO, Jordan KE, Coley CW. Roughness of Molecular Property Landscapes and Its Impact on Modellability. J Chem Inf Model 2022; 62:4660-4671. [DOI: 10.1021/acs.jcim.2c00903] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/27/2022]
Affiliation(s)
- Matteo Aldeghi
- Department of Chemical Engineering, Massachusetts Institute of Technology, Cambridge, Massachusetts 02139, United States
| | - David E. Graff
- Department of Chemistry and Chemical Biology, Harvard University, Cambridge, Massachusetts 02138, United States
- Department of Chemical Engineering, Massachusetts Institute of Technology, Cambridge, Massachusetts 02139, United States
| | - Nathan Frey
- Lincoln Laboratory, Massachusetts Institute of Technology, Lexington, Massachusetts 02421, United States
| | - Joseph A. Morrone
- IBM Thomas J. Watson Research Center, Yorktown Heights, New York 10598, United States
| | | | - Kirk E. Jordan
- IBM Thomas J. Watson Research Center, Cambridge, Massachusetts 02142, United States
| | - Connor W. Coley
- Department of Chemistry and Chemical Biology, Harvard University, Cambridge, Massachusetts 02138, United States
- Department of Electrical Engineering and Computer Science, Massachusetts Institute of Technology, Cambridge, Massachusetts 02139, United States
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8
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Iwata H, Matsuo T, Mamada H, Motomura T, Matsushita M, Fujiwara T, Maeda K, Handa K. Predicting Total Drug Clearance and Volumes of Distribution Using the Machine Learning-Mediated Multimodal Method through the Imputation of Various Nonclinical Data. J Chem Inf Model 2022; 62:4057-4065. [PMID: 35993595 PMCID: PMC9472274 DOI: 10.1021/acs.jcim.2c00318] [Citation(s) in RCA: 12] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/30/2022]
Abstract
![]()
Pharmacokinetic research plays an important role in the
development
of new drugs. Accurate predictions of human pharmacokinetic parameters
are essential for the success of clinical trials. Clearance (CL) and
volume of distribution (Vd) are important factors for evaluating pharmacokinetic
properties, and many previous studies have attempted to use computational
methods to extrapolate these values from nonclinical laboratory animal
models to human subjects. However, it is difficult to obtain sufficient,
comprehensive experimental data from these animal models, and many
studies are missing critical values. This means that studies using
nonclinical data as explanatory variables can only apply a small number
of compounds to their model training. In this study, we perform missing-value
imputation and feature selection on nonclinical data to increase the
number of training compounds and nonclinical datasets available for
these kinds of studies. We could obtain novel models for total body
clearance (CLtot) and steady-state Vd (Vdss)
(CLtot: geometric mean fold error [GMFE], 1.92; percentage
within 2-fold error, 66.5%; Vdss: GMFE, 1.64; percentage
within 2-fold error, 71.1%). These accuracies were comparable to the
conventional animal scale-up models. Then, this method differs from
animal scale-up methods because it does not require animal experiments,
which continue to become more strictly regulated as time passes.
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Affiliation(s)
- Hiroaki Iwata
- Graduate School of Medicine, Kyoto University, 53 Shogoin-kawaharacho, Sakyo-ku, Kyoto 606-8507, Japan
| | - Tatsuru Matsuo
- Fujitsu Ltd., 4-1-1 Kamikodanaka, Nakahara-ku, Kawasaki-shi, Kanagawa 211-8588, Japan
| | - Hideaki Mamada
- DMPK Research Laboratories, Central Pharmaceutical Research Institute, Japan Tobacco Inc., 1-1, Murasaki-cho, Takatsuki, Osaka 569-1125, Japan
| | - Takahisa Motomura
- Central Pharmaceutical Research Institute, Japan Tobacco Inc., 1-1, Murasaki-cho, Takatsuki, Osaka 569-1125, Japan
| | - Mayumi Matsushita
- Fujitsu Ltd., 4-1-1 Kamikodanaka, Nakahara-ku, Kawasaki-shi, Kanagawa 211-8588, Japan
| | - Takeshi Fujiwara
- Graduate School of Medicine, Kyoto University, 53 Shogoin-kawaharacho, Sakyo-ku, Kyoto 606-8507, Japan
| | - Kazuya Maeda
- Graduate School of Pharmaceutical Sciences, Department of Molecular Pharmacokinetics, The University of Tokyo, 7-3-1 Hongo, Bunkyo-ku, Tokyo 113-0033, Japan
| | - Koichi Handa
- Toxicology & DMPK Research Department, Teijin Institute for Bio-medical Research, Teijin Pharma Limited, 4-3-2 Asahigaoka, Hino-shi, Tokyo 191-8512, Japan
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Lim S, Lee S, Piao Y, Choi M, Bang D, Gu J, Kim S. On modeling and utilizing chemical compound information with deep learning technologies: A task-oriented approach. Comput Struct Biotechnol J 2022; 20:4288-4304. [PMID: 36051875 PMCID: PMC9399946 DOI: 10.1016/j.csbj.2022.07.049] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/02/2022] [Revised: 07/29/2022] [Accepted: 07/29/2022] [Indexed: 11/22/2022] Open
Abstract
A large number of chemical compounds are available in databases such as PubChem and ZINC. However, currently known compounds, though large, represent only a fraction of possible compounds, which is known as chemical space. Many of these compounds in the databases are annotated with properties and assay data that can be used for drug discovery efforts. For this goal, a number of machine learning algorithms have been developed and recent deep learning technologies can be effectively used to navigate chemical space, especially for unknown chemical compounds, in terms of drug-related tasks. In this article, we survey how deep learning technologies can model and utilize chemical compound information in a task-oriented way by exploiting annotated properties and assay data in the chemical compounds databases. We first compile what kind of tasks are trying to be accomplished by machine learning methods. Then, we survey deep learning technologies to show their modeling power and current applications for accomplishing drug related tasks. Next, we survey deep learning techniques to address the insufficiency issue of annotated data for more effective navigation of chemical space. Chemical compound information alone may not be powerful enough for drug related tasks, thus we survey what kind of information, such as assay and gene expression data, can be used to improve the prediction power of deep learning models. Finally, we conclude this survey with four important newly developed technologies that are yet to be fully incorporated into computational analysis of chemical information.
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Affiliation(s)
- Sangsoo Lim
- Bioinformatics Institute, Seoul National University, Gwanak-ro 1, Gwanak-gu, Seoul 08826, South Korea
| | - Sangseon Lee
- Institute of Computer Technology, Seoul National University, Gwanak-ro 1, Gwanak-gu, Seoul 08826, South Korea
| | - Yinhua Piao
- Department of Computer Science and Engineering, Seoul National University, Gwanak-ro 1, Gwanak-gu, Seoul 08826, South Korea
| | - MinGyu Choi
- Department of Chemistry, Seoul National University, Gwanak-ro 1, Gwanak-gu, Seoul 08826, South Korea
- AIGENDRUG Co., Ltd., Gwanak-ro 1, Gwanak-gu, Seoul 08826, South Korea
| | - Dongmin Bang
- Interdisciplinary Program in Bioinformatics, Seoul National University, Gwanak-ro 1, Gwanak-gu, Seoul 08826, South Korea
| | - Jeonghyeon Gu
- Interdisciplinary Program in Artificial Intelligence, Seoul National University, Gwanak-ro 1, Gwanak-gu, Seoul 08826, South Korea
| | - Sun Kim
- Department of Computer Science and Engineering, Seoul National University, Gwanak-ro 1, Gwanak-gu, Seoul 08826, South Korea
- Interdisciplinary Program in Artificial Intelligence, Seoul National University, Gwanak-ro 1, Gwanak-gu, Seoul 08826, South Korea
- MOGAM Institute for Biomedical Research, Yong-in 16924, South Korea
- AIGENDRUG Co., Ltd., Gwanak-ro 1, Gwanak-gu, Seoul 08826, South Korea
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10
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Gao W, Hu H, Dai L, He M, Yuan H, Zhang H, Liao J, Wen B, Li Y, Palmisano M, Traore MDM, Zhou S, Sun D. Structure‒tissue exposure/selectivity relationship (STR) correlates with clinical efficacy/safety. Acta Pharm Sin B 2022; 12:2462-2478. [PMID: 35646532 PMCID: PMC9136610 DOI: 10.1016/j.apsb.2022.02.015] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/27/2021] [Revised: 01/23/2022] [Accepted: 02/12/2022] [Indexed: 11/17/2022] Open
Abstract
Drug optimization, which improves drug potency/specificity by structure‒activity relationship (SAR) and drug-like properties, is rigorously performed to select drug candidates for clinical trials. However, the current drug optimization may overlook the structure‒tissue exposure/selectivity-relationship (STR) in disease-targeted tissues vs. normal tissues, which may mislead the drug candidate selection and impact the balance of clinical efficacy/toxicity. In this study, we investigated the STR in correlation with observed clinical efficacy/toxicity using seven selective estrogen receptor modulators (SERMs) that have similar structures, same molecular target, and similar/different pharmacokinetics. The results showed that drug's plasma exposure was not correlated with drug's exposures in the target tissues (tumor, fat pad, bone, uterus), while tissue exposure/selectivity of SERMs was correlated with clinical efficacy/safety. Slight structure modifications of four SERMs did not change drug's plasma exposure but altered drug's tissue exposure/selectivity. Seven SERMs with high protein binding showed higher accumulation in tumors compared to surrounding normal tissues, which is likely due to tumor EPR effect of protein-bound drugs. These suggest that STR alters drug's tissue exposure/selectivity in disease-targeted tissues vs. normal tissues impacting clinical efficacy/toxicity. Drug optimization needs to balance the SAR and STR in selecting drug candidate for clinical trial to improve success of clinical drug development.
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Affiliation(s)
- Wei Gao
- Department of Pharmaceutical Sciences, College of Pharmacy, University of Michigan, Ann Arbor, MI 48109, USA
| | - Hongxiang Hu
- Department of Pharmaceutical Sciences, College of Pharmacy, University of Michigan, Ann Arbor, MI 48109, USA
| | - Lipeng Dai
- Department of Pharmaceutical Sciences, College of Pharmacy, University of Michigan, Ann Arbor, MI 48109, USA
| | - Miao He
- Department of Pharmaceutical Sciences, College of Pharmacy, University of Michigan, Ann Arbor, MI 48109, USA
| | - Hebao Yuan
- Department of Pharmaceutical Sciences, College of Pharmacy, University of Michigan, Ann Arbor, MI 48109, USA
| | - Huixia Zhang
- Department of Pharmaceutical Sciences, College of Pharmacy, University of Michigan, Ann Arbor, MI 48109, USA
| | - Jinhui Liao
- Department of Pharmaceutical Sciences, College of Pharmacy, University of Michigan, Ann Arbor, MI 48109, USA
| | - Bo Wen
- Department of Pharmaceutical Sciences, College of Pharmacy, University of Michigan, Ann Arbor, MI 48109, USA
| | - Yan Li
- Translational Development and Clinical Pharmacology, Bristol Myers Squibb, Summit, NJ 07920, USA
| | - Maria Palmisano
- Translational Development and Clinical Pharmacology, Bristol Myers Squibb, Summit, NJ 07920, USA
| | - Mohamed Dit Mady Traore
- Department of Pharmaceutical Sciences, College of Pharmacy, University of Michigan, Ann Arbor, MI 48109, USA
| | - Simon Zhou
- Translational Development and Clinical Pharmacology, Bristol Myers Squibb, Summit, NJ 07920, USA
| | - Duxin Sun
- Department of Pharmaceutical Sciences, College of Pharmacy, University of Michigan, Ann Arbor, MI 48109, USA
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11
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Machine learning & deep learning in data-driven decision making of drug discovery and challenges in high-quality data acquisition in the pharmaceutical industry. Future Med Chem 2021; 14:245-270. [PMID: 34939433 DOI: 10.4155/fmc-2021-0243] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/10/2023] Open
Abstract
Predicting novel small molecule bioactivities for the target deconvolution, hit-to-lead optimization in drug discovery research, requires molecular representation. Previous reports have demonstrated that machine learning (ML) and deep learning (DL) have substantial implications in virtual screening, peptide synthesis, drug ADMET screening and biomarker discovery. These strategies can increase the positive outcomes in the drug discovery process without false-positive rates and can be achieved in a cost-effective way with a minimum duration of time by high-quality data acquisition. This review substantially discusses the recent updates in AI tools as cheminformatics application in medicinal chemistry for the data-driven decision making of drug discovery and challenges in high-quality data acquisition in the pharmaceutical industry while improving small-molecule bioactivities and properties.
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12
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Abstract
This review provides the feasible literature on drug discovery through ML tools and techniques that are enforced in every phase of drug development to accelerate the research process and deduce the risk and expenditure in clinical trials. Machine learning techniques improve the decision-making in pharmaceutical data across various applications like QSAR analysis, hit discoveries, de novo drug architectures to retrieve accurate outcomes. Target validation, prognostic biomarkers, digital pathology are considered under problem statements in this review. ML challenges must be applicable for the main cause of inadequacy in interpretability outcomes that may restrict the applications in drug discovery. In clinical trials, absolute and methodological data must be generated to tackle many puzzles in validating ML techniques, improving decision-making, promoting awareness in ML approaches, and deducing risk failures in drug discovery.
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Affiliation(s)
- Suresh Dara
- Department of Computer Science and Engineering, B V Raju Institute of Technology, Narsapur, Medak, 502313 Telangana India
| | - Swetha Dhamercherla
- Department of Computer Science and Engineering, B V Raju Institute of Technology, Narsapur, Medak, 502313 Telangana India
| | - Surender Singh Jadav
- Centre for Molecular Cancer Research (CMCR) and Vishnu Institute of Pharmaceutical Education and Research (VIPER), Narsapur, Medak, 502313 Telangana India
| | - CH Madhu Babu
- Department of Computer Science and Engineering, B V Raju Institute of Technology, Narsapur, Medak, 502313 Telangana India
| | - Mohamed Jawed Ahsan
- Department of Pharmaceutical Chemistry, Maharishi Arvind College of Pharmacy, Jaipur, 302023 Rajasthan India
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13
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Prediction of Total Drug Clearance in Humans Using Animal Data: Proposal of a Multimodal Learning Method Based on Deep Learning. J Pharm Sci 2021; 110:1834-1841. [PMID: 33497658 DOI: 10.1016/j.xphs.2021.01.020] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/04/2020] [Revised: 01/14/2021] [Accepted: 01/19/2021] [Indexed: 12/20/2022]
Abstract
Research into pharmacokinetics plays an important role in the development process of new drugs. Accurately predicting human pharmacokinetic parameters from preclinical data can increase the success rate of clinical trials. Since clearance (CL) which indicates the capacity of the entire body to process a drug is one of the most important parameters, many methods have been developed. However, there are still rooms to be improved for practical use in drug discovery research; "improving CL prediction accuracy" and "understanding the chemical structure of compounds in terms of pharmacokinetics". To improve those, this research proposes a multimodal learning method based on deep learning that takes not only the chemical structure of a drug but also rat CL as inputs. Good results were obtained compared with the conventional animal scale-up method; the geometric mean fold error was 2.68 and the proportion of compounds with prediction errors of 2-fold or less was 48.5%. Furthermore, it was found to be possible to infer the partial structure useful for CL prediction by a structure contributing factor inference method. The validity of these results of structural interpretation of metabolic stability was confirmed by chemists.
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14
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Mathew S, Tess D, Burchett W, Chang G, Woody N, Keefer C, Orozco C, Lin J, Jordan S, Yamazaki S, Jones R, Di L. Evaluation of Prediction Accuracy for Volume of Distribution in Rat and Human Using In Vitro, In Vivo, PBPK and QSAR Methods. J Pharm Sci 2020; 110:1799-1823. [PMID: 33338491 DOI: 10.1016/j.xphs.2020.12.005] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/13/2020] [Revised: 11/17/2020] [Accepted: 12/03/2020] [Indexed: 10/22/2022]
Abstract
Volume of distribution at steady state (Vss) is an important pharmacokinetic parameter of a drug candidate. In this study, Vss prediction accuracy was evaluated by using: (1) seven methods for rat with 56 compounds, (2) four methods for human with 1276 compounds, and (3) four in vivo methods and three Kp (partition coefficient) scalar methods from scaling of three preclinical species with 125 compounds. The results showed that the global QSAR models outperformed the PBPK methods. Tissue fraction unbound (fu,t) method with adipose and muscle also provided high Vss prediction accuracy. Overall, the high performing methods for human Vss prediction are the global QSAR models, Øie-Tozer and equivalency methods from scaling of preclinical species, as well as PBPK methods with Kp scalar from preclinical species. Certain input parameter ranges rendered PBPK models inaccurate due to mass balance issues. These were addressed using appropriate theoretical limit checks. Prediction accuracy of tissue Kp were also examined. The fu,t method predicted Kp values more accurately than the PBPK methods for adipose, heart and muscle. All the methods overpredicted brain Kp and underpredicted liver Kp due to transporter effects. Successful Vss prediction involves strategic integration of in silico, in vitro and in vivo approaches.
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Affiliation(s)
- Shibin Mathew
- Pharmacokinetics, Dynamics and Metabolism, Pfizer Worldwide Research and Development, Cambridge, MA 02139, USA
| | - David Tess
- Pharmacokinetics, Dynamics and Metabolism, Pfizer Worldwide Research and Development, Cambridge, MA 02139, USA
| | - Woodrow Burchett
- Early Clinical Development, Pfizer Worldwide Research and Development, Groton, CT 06340, USA
| | - George Chang
- Pharmacokinetics, Dynamics and Metabolism, Pfizer Worldwide Research and Development, Groton, CT 06340, USA
| | - Nathaniel Woody
- Pharmacokinetics, Dynamics and Metabolism, Pfizer Worldwide Research and Development, Groton, CT 06340, USA
| | - Christopher Keefer
- Pharmacokinetics, Dynamics and Metabolism, Pfizer Worldwide Research and Development, Groton, CT 06340, USA
| | - Christine Orozco
- Pharmacokinetics, Dynamics and Metabolism, Pfizer Worldwide Research and Development, Groton, CT 06340, USA
| | - Jian Lin
- Pharmacokinetics, Dynamics and Metabolism, Pfizer Worldwide Research and Development, Groton, CT 06340, USA
| | - Samantha Jordan
- Pharmacokinetics, Dynamics and Metabolism, Pfizer Worldwide Research and Development, Groton, CT 06340, USA
| | - Shinji Yamazaki
- Pharmacokinetics, Dynamics and Metabolism, Pfizer Worldwide Research and Development, San Diego, CA 92121, USA
| | - Rhys Jones
- Pharmacokinetics, Dynamics and Metabolism, Pfizer Worldwide Research and Development, San Diego, CA 92121, USA
| | - Li Di
- Pharmacokinetics, Dynamics and Metabolism, Pfizer Worldwide Research and Development, Groton, CT 06340, USA.
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15
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Lombardo F, Bentzien J, Berellini G, Muegge I. In Silico Models of Human PK Parameters. Prediction of Volume of Distribution Using an Extensive Data Set and a Reduced Number of Parameters. J Pharm Sci 2020; 110:500-509. [PMID: 32891631 DOI: 10.1016/j.xphs.2020.08.023] [Citation(s) in RCA: 21] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/29/2020] [Revised: 08/27/2020] [Accepted: 08/27/2020] [Indexed: 12/15/2022]
Abstract
A novel, descriptor-parsimonious in silico model to predict human VDss (volume of distribution at steady-state) has been derived and thoroughly tested in a quasi-prospective regimen using an independent test set of 213 compounds. The model performs on par with a former benchmark model that relied on far more descriptors. As a result, the new random forest model relying on only six descriptors allows for interpretations that help chemists to design compounds with desired human VDss values. A comparison of in silico predictions of VDss with models using in vitro derived descriptors or in vivo scaling methods supports the strength of the in-silico approach, considering its resource- and animal-sparing nature. The strong performance of the in silico VDss models on structurally novel compounds supports the high degree of confidence that can be placed in using in silico human VDss predictions for compound design and human dose predictions.
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Affiliation(s)
- Franco Lombardo
- Drug Metabolism and Bioanalysis Group, Alkermes Inc, Waltham, MA 02451, USA.
| | - Jörg Bentzien
- Modeling and Informatics Group, Alkermes Inc, Waltham, MA 02451, USA
| | - Giuliano Berellini
- Drug Metabolism and Bioanalysis Group, Alkermes Inc, Waltham, MA 02451, USA
| | - Ingo Muegge
- Modeling and Informatics Group, Alkermes Inc, Waltham, MA 02451, USA
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16
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Davies M, Jones RDO, Grime K, Jansson-Löfmark R, Fretland AJ, Winiwarter S, Morgan P, McGinnity DF. Improving the Accuracy of Predicted Human Pharmacokinetics: Lessons Learned from the AstraZeneca Drug Pipeline Over Two Decades. Trends Pharmacol Sci 2020; 41:390-408. [PMID: 32359836 DOI: 10.1016/j.tips.2020.03.004] [Citation(s) in RCA: 53] [Impact Index Per Article: 13.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/29/2019] [Revised: 03/20/2020] [Accepted: 03/25/2020] [Indexed: 01/15/2023]
Abstract
During drug discovery and prior to the first human dose of a novel candidate drug, the pharmacokinetic (PK) behavior of the drug in humans is predicted from preclinical data. This helps to inform the likelihood of achieving therapeutic exposures in early clinical development. Once clinical data are available, the observed human PK are compared with predictions, providing an opportunity to assess and refine prediction methods. Application of best practice in experimental data generation and predictive methodologies, and a focus on robust mechanistic understanding of the candidate drug disposition properties before nomination to clinical development, have led to maximizing the probability of successful PK predictions so that 83% of AstraZeneca drug development projects progress in the clinic with no PK issues; and 71% of key PK parameter predictions [64% of area under the curve (AUC) predictions; 78% of maximum concentration (Cmax) predictions; and 70% of half-life predictions] are accurate to within twofold. Here, we discuss methods to predict human PK used by AstraZeneca, how these predictions are assessed and what can be learned from evaluating the predictions for 116 candidate drugs.
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Affiliation(s)
- Michael Davies
- DMPK, Research and Early Development, Oncology R&D, AstraZeneca, Cambridge, UK.
| | - Rhys D O Jones
- DMPK, Research and Early Development, Oncology R&D, AstraZeneca, Cambridge, UK
| | - Ken Grime
- DMPK, Research and Early Development, Respiratory, Inflammation and Autoimmune, BioPharmaceuticals R&D, AstraZeneca, Gothenburg, Sweden
| | - Rasmus Jansson-Löfmark
- DMPK, Research and Early Development, Cardiovascular, Renal and Metabolic, BioPharmaceuticals R&D, AstraZeneca, Gothenburg, Sweden
| | - Adrian J Fretland
- DMPK, Research and Early Development, Oncology R&D, AstraZeneca, Boston, MA, USA
| | - Susanne Winiwarter
- DMPK, Research and Early Development, Cardiovascular, Renal and Metabolic, BioPharmaceuticals R&D, AstraZeneca, Gothenburg, Sweden
| | - Paul Morgan
- Mechanistic Safety and ADME Sciences, Clinical Pharmacology and Safety Sciences, R&D, AstraZeneca, Cambridge, UK
| | - Dermot F McGinnity
- DMPK, Research and Early Development, Oncology R&D, AstraZeneca, Cambridge, UK
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17
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Berellini G, Lombardo F. An Accurate In Vitro Prediction of Human VDss Based on the Øie–Tozer Equation and Primary Physicochemical Descriptors. 3. Analysis and Assessment of Predictivity on a Large Dataset. Drug Metab Dispos 2019; 47:1380-1387. [DOI: 10.1124/dmd.119.088914] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/06/2019] [Accepted: 09/27/2019] [Indexed: 12/24/2022] Open
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18
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Wang Y, Liu H, Fan Y, Chen X, Yang Y, Zhu L, Zhao J, Chen Y, Zhang Y. In Silico Prediction of Human Intravenous Pharmacokinetic Parameters with Improved Accuracy. J Chem Inf Model 2019; 59:3968-3980. [DOI: 10.1021/acs.jcim.9b00300] [Citation(s) in RCA: 38] [Impact Index Per Article: 7.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/15/2022]
Affiliation(s)
- Yuchen Wang
- School of Science, China Pharmaceutical University, 639 Longmian Avenue, Nanjing, Jiangsu 211198, China
| | - Haichun Liu
- School of Science, China Pharmaceutical University, 639 Longmian Avenue, Nanjing, Jiangsu 211198, China
| | - Yuanrong Fan
- School of Science, China Pharmaceutical University, 639 Longmian Avenue, Nanjing, Jiangsu 211198, China
| | - Xingye Chen
- School of Science, China Pharmaceutical University, 639 Longmian Avenue, Nanjing, Jiangsu 211198, China
| | - Yan Yang
- School of Science, China Pharmaceutical University, 639 Longmian Avenue, Nanjing, Jiangsu 211198, China
| | - Lu Zhu
- School of Science, China Pharmaceutical University, 639 Longmian Avenue, Nanjing, Jiangsu 211198, China
| | - Junnan Zhao
- School of Science, China Pharmaceutical University, 639 Longmian Avenue, Nanjing, Jiangsu 211198, China
| | - Yadong Chen
- School of Science, China Pharmaceutical University, 639 Longmian Avenue, Nanjing, Jiangsu 211198, China
| | - Yanmin Zhang
- School of Science, China Pharmaceutical University, 639 Longmian Avenue, Nanjing, Jiangsu 211198, China
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19
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Simeon S, Montanari D, Gleeson MP. Investigation of Factors Affecting the Performance of
in silico
Volume Distribution QSAR Models for Human, Rat, Mouse, Dog & Monkey. Mol Inform 2019; 38:e1900059. [DOI: 10.1002/minf.201900059] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/23/2019] [Accepted: 07/03/2019] [Indexed: 01/09/2023]
Affiliation(s)
- Saw Simeon
- Interdisciplinary Graduate Program in Bioscience, Faculty of ScienceKasetsart University Bangkok 10900 Thailand
- Center for Advanced Studies in Nanotechnology for Chemical, Food and Agricultural Industries, KU Institute for Advanced StudiesKasetsart University Bangkok 10900 Thailand
| | - Dino Montanari
- DMPK and Bioanalysis, Aptuit Via Alessandro Fleming, 4 37135 Verona VR Italy
| | - Matthew Paul Gleeson
- Department of Chemistry, Faculty of ScienceKasetsart University Bangkok 10900 Thailand
- Department of Biomedical Engineering, Faculty of EngineeringKing Mongkut's Institute of Technology Ladkrabang Bangkok 10520 Thailand
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20
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Yang X, Wang Y, Byrne R, Schneider G, Yang S. Concepts of Artificial Intelligence for Computer-Assisted Drug Discovery. Chem Rev 2019; 119:10520-10594. [PMID: 31294972 DOI: 10.1021/acs.chemrev.8b00728] [Citation(s) in RCA: 346] [Impact Index Per Article: 69.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/05/2023]
Abstract
Artificial intelligence (AI), and, in particular, deep learning as a subcategory of AI, provides opportunities for the discovery and development of innovative drugs. Various machine learning approaches have recently (re)emerged, some of which may be considered instances of domain-specific AI which have been successfully employed for drug discovery and design. This review provides a comprehensive portrayal of these machine learning techniques and of their applications in medicinal chemistry. After introducing the basic principles, alongside some application notes, of the various machine learning algorithms, the current state-of-the art of AI-assisted pharmaceutical discovery is discussed, including applications in structure- and ligand-based virtual screening, de novo drug design, physicochemical and pharmacokinetic property prediction, drug repurposing, and related aspects. Finally, several challenges and limitations of the current methods are summarized, with a view to potential future directions for AI-assisted drug discovery and design.
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Affiliation(s)
- Xin Yang
- State Key Laboratory of Biotherapy and Cancer Center, West China Hospital , Sichuan University , Chengdu , Sichuan 610041 , China
| | - Yifei Wang
- State Key Laboratory of Biotherapy and Cancer Center, West China Hospital , Sichuan University , Chengdu , Sichuan 610041 , China
| | - Ryan Byrne
- ETH Zurich , Department of Chemistry and Applied Biosciences , Vladimir-Prelog-Weg 4 , CH-8093 Zurich , Switzerland
| | - Gisbert Schneider
- ETH Zurich , Department of Chemistry and Applied Biosciences , Vladimir-Prelog-Weg 4 , CH-8093 Zurich , Switzerland
| | - Shengyong Yang
- State Key Laboratory of Biotherapy and Cancer Center, West China Hospital , Sichuan University , Chengdu , Sichuan 610041 , China
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21
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Petito ES, Foster DJR, Ward MB, Sykes MJ. Molecular Modeling Approaches for the Prediction of Selected Pharmacokinetic Properties. Curr Top Med Chem 2019; 18:2230-2238. [PMID: 30569859 DOI: 10.2174/1568026619666181220105726] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/17/2018] [Revised: 11/22/2018] [Accepted: 12/15/2018] [Indexed: 02/06/2023]
Abstract
Poor profiles of potential drug candidates, including pharmacokinetic properties, have been acknowledged as a significant hindrance to the development of modern therapeutics. Contemporary drug discovery and development would be incomplete without the aid of molecular modeling (in-silico) techniques, allowing the prediction of pharmacokinetic properties such as clearance, unbound fraction, volume of distribution and bioavailability. As with all models, in-silico approaches are subject to their interpretability, a trait that must be balanced with accuracy when considering the development of new methods. The best models will always require reliable data to inform them, presenting significant challenges, particularly when appropriate in-vitro or in-vivo data may be difficult or time-consuming to obtain. This article seeks to review some of the key in-silico techniques used to predict key pharmacokinetic properties and give commentary on the current and future directions of the field.
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Affiliation(s)
- Emilio S Petito
- School of Pharmacy and Medical Sciences, Division of Health Sciences, University of South Australia Cancer Research Institute, Adelaide, South Australia 5001, Australia
| | - David J R Foster
- School of Pharmacy and Medical Sciences, Division of Health Sciences, University of South Australia Cancer Research Institute, Adelaide, South Australia 5001, Australia
| | - Michael B Ward
- School of Pharmacy and Medical Sciences, Division of Health Sciences, University of South Australia Cancer Research Institute, Adelaide, South Australia 5001, Australia
| | - Matthew J Sykes
- School of Pharmacy and Medical Sciences, Division of Health Sciences, University of South Australia Cancer Research Institute, Adelaide, South Australia 5001, Australia
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22
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Ye Z, Yang Y, Li X, Cao D, Ouyang D. An Integrated Transfer Learning and Multitask Learning Approach for Pharmacokinetic Parameter Prediction. Mol Pharm 2019; 16:533-541. [PMID: 30571137 DOI: 10.1021/acs.molpharmaceut.8b00816] [Citation(s) in RCA: 45] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/02/2023]
Abstract
BACKGROUND Pharmacokinetic evaluation is one of the key processes in drug discovery and development. However, current absorption, distribution, metabolism, and excretion prediction models still have limited accuracy. AIM This study aims to construct an integrated transfer learning and multitask learning approach for developing quantitative structure-activity relationship models to predict four human pharmacokinetic parameters. METHODS A pharmacokinetic data set included 1104 U.S. FDA approved small molecule drugs. The data set included four human pharmacokinetic parameter subsets (oral bioavailability, plasma protein binding rate, apparent volume of distribution at steady-state, and elimination half-life). The pretrained model was trained on over 30 million bioactivity data entries. An integrated transfer learning and multitask learning approach was established to enhance the model generalization. RESULTS The pharmacokinetic data set was split into three parts (60:20:20) for training, validation, and testing by the improved maximum dissimilarity algorithm with the representative initial set selection algorithm and the weighted distance function. The multitask learning techniques enhanced the model predictive ability. The integrated transfer learning and multitask learning model demonstrated the best accuracies, because deep neural networks have the general feature extraction ability; transfer learning and multitask learning improve the model generalization. CONCLUSIONS The integrated transfer learning and multitask learning approach with the improved data set splitting algorithm was first introduced to predict the pharmacokinetic parameters. This method can be further employed in drug discovery and development.
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Affiliation(s)
- Zhuyifan Ye
- State Key Laboratory of Quality Research in Chinese Medicine, Institute of Chinese Medical Sciences (ICMS) , University of Macau , Macau , China
| | - Yilong Yang
- State Key Laboratory of Quality Research in Chinese Medicine, Institute of Chinese Medical Sciences (ICMS) , University of Macau , Macau , China.,Department of Computer and Information Science, Faculty of Science and Technology , University of Macau , Macau , China
| | - Xiaoshan Li
- Department of Computer and Information Science, Faculty of Science and Technology , University of Macau , Macau , China
| | - Dongsheng Cao
- Xiangya School of Pharmaceutical Sciences , Central South University , No. 172, Tongzipo Road , Yuelu District, Changsha 410083 , People's Republic of China
| | - Defang Ouyang
- State Key Laboratory of Quality Research in Chinese Medicine, Institute of Chinese Medical Sciences (ICMS) , University of Macau , Macau , China
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23
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Lombardo F, Berellini G, Obach RS. Trend Analysis of a Database of Intravenous Pharmacokinetic Parameters in Humans for 1352 Drug Compounds. Drug Metab Dispos 2018; 46:1466-1477. [PMID: 30115648 DOI: 10.1124/dmd.118.082966] [Citation(s) in RCA: 79] [Impact Index Per Article: 13.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/08/2018] [Accepted: 08/09/2018] [Indexed: 11/22/2022] Open
Abstract
We report a trend analysis of human intravenous pharmacokinetic data on a data set of 1352 drugs. The aim in building this data set and its detailed analysis was to provide, as in the previous case published in 2008, an extended, robust, and accurate resource that could be applied by drug metabolism, clinical pharmacology, and medicinal chemistry scientists to a variety of scaling approaches. All in vivo data were obtained or derived from original references, either through the literature or regulatory agency reports, exclusively from studies utilizing intravenous administration. Plasma protein binding data were collected from other available sources to supplement these pharmacokinetic data. These parameters were analyzed concurrently with a range of physicochemical properties, and resultant trends and patterns within the data are presented. In addition, the date of first disclosure of each molecule was reported and the potential "temporal" impact on data trends was analyzed. The findings reported here are consistent with earlier described trends between pharmacokinetic behavior and physicochemical properties. Furthermore, the availability of a large data set of pharmacokinetic data in humans will be important to further pursue analyses of physicochemical properties, trends, and modeling efforts and should propel our deeper understanding (especially in terms of clearance) of the absorption, distribution, metabolism, and excretion behavior of drug compounds.
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Affiliation(s)
- Franco Lombardo
- Drug Metabolism and Bioanalysis Group, Alkermes Inc., Waltham, Massachusetts (F.L.); Computational Chemistry Group, Biogen Inc., Cambridge, Massachusetts (G.B.); and Pharmacokinetics Dynamics and Metabolism Department, Groton Laboratories, Pfizer Global Research and Development, Groton, Connecticut (R.S.O.)
| | - Giuliano Berellini
- Drug Metabolism and Bioanalysis Group, Alkermes Inc., Waltham, Massachusetts (F.L.); Computational Chemistry Group, Biogen Inc., Cambridge, Massachusetts (G.B.); and Pharmacokinetics Dynamics and Metabolism Department, Groton Laboratories, Pfizer Global Research and Development, Groton, Connecticut (R.S.O.)
| | - R Scott Obach
- Drug Metabolism and Bioanalysis Group, Alkermes Inc., Waltham, Massachusetts (F.L.); Computational Chemistry Group, Biogen Inc., Cambridge, Massachusetts (G.B.); and Pharmacokinetics Dynamics and Metabolism Department, Groton Laboratories, Pfizer Global Research and Development, Groton, Connecticut (R.S.O.)
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24
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Luque Ruiz I, Gómez-Nieto MÁ. Robust QSAR prediction models for volume of distribution at steady state in humans using relative distance measurements. SAR AND QSAR IN ENVIRONMENTAL RESEARCH 2018; 29:529-550. [PMID: 30044137 DOI: 10.1080/1062936x.2018.1494038] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/09/2018] [Accepted: 06/25/2018] [Indexed: 06/08/2023]
Abstract
The building of quantitative structure-activity relationship (QSAR) models for the in silico prediction of volume distribution for drugs at steady-state levels is vital for the selection of potential drugs at the synthesis stage. Using molecular descriptor matrixes, some regression models presenting low accuracy have been proposed, mainly due to the difficulty of compiling an appropriate dataset and the lack of information on dataset representation. In this paper, we use a benchmark dataset of very diverse drugs for the development of predictive models for volume distribution based on the use of relative distance matrixes as the input data to QSAR algorithms. Support vector machine, complex tree, bagged tree and Gaussian process regression algorithms were tested for fingerprint, similarity and relative distance matrixes used as input data, and the results of the built models were compared. Relative distance matrixes generated robust regression models in the training and external validation stages performed using cross-validation, obtaining values for correlation coefficient, bias, slope and root-mean-square error close to the ideal. Relative distance matrixes were also used for the design of classification models, obtaining excellent results with values of accuracy and area under receiver operating characteristic (AUC) close to 100%.
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Affiliation(s)
- I Luque Ruiz
- a Department of Computing and Numerical Analysis , University of Córdoba, Campus de Rabanales , Albert Einstein building, E-14071 , Córdoba , Spain
| | - M Á Gómez-Nieto
- a Department of Computing and Numerical Analysis , University of Córdoba, Campus de Rabanales , Albert Einstein building, E-14071 , Córdoba , Spain
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25
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Panteleev J, Gao H, Jia L. Recent applications of machine learning in medicinal chemistry. Bioorg Med Chem Lett 2018; 28:2807-2815. [PMID: 30122222 DOI: 10.1016/j.bmcl.2018.06.046] [Citation(s) in RCA: 55] [Impact Index Per Article: 9.2] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/10/2018] [Revised: 06/24/2018] [Accepted: 06/26/2018] [Indexed: 12/20/2022]
Abstract
In recent decades, artificial intelligence and machine learning have played a significant role in increasing the efficiency of processes across a wide spectrum of industries. When it comes to the pharmaceutical and biotechnology sectors, numerous tools enabled by advancement of computer science have been developed and are now routinely utilized. However, there are many aspects of the drug discovery process, which can further benefit from refinement of computational methods and tools, as well as improvement of accessibility of these new technologies. In this review, examples of recent developments in machine learning application are described, which have the potential to impact different parts of the drug discovery and development flow scheme. Notably, new deep learning-based approaches across compound design and synthesis, prediction of binding, activity and ADMET properties, as well as applications of genetic algorithms are highlighted.
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Affiliation(s)
- Jane Panteleev
- Amgen Discovery Research, 360 Binney St., Cambridge, MA 02141, USA
| | - Hua Gao
- Amgen Discovery Research, 360 Binney St., Cambridge, MA 02141, USA
| | - Lei Jia
- Amgen Discovery Research, One Amgen Center Dr., Thousand Oaks, CA 91320, USA.
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26
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Li M, Zhang H, Chen B, Wu Y, Guan L. Prediction of pKa Values for Neutral and Basic Drugs based on Hybrid Artificial Intelligence Methods. Sci Rep 2018; 8:3991. [PMID: 29507318 PMCID: PMC5838250 DOI: 10.1038/s41598-018-22332-7] [Citation(s) in RCA: 30] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/04/2017] [Accepted: 02/21/2018] [Indexed: 11/23/2022] Open
Abstract
The pKa value of drugs is an important parameter in drug design and pharmacology. In this paper, an improved particle swarm optimization (PSO) algorithm was proposed based on the population entropy diversity. In the improved algorithm, when the population entropy was higher than the set maximum threshold, the convergence strategy was adopted; when the population entropy was lower than the set minimum threshold the divergence strategy was adopted; when the population entropy was between the maximum and minimum threshold, the self-adaptive adjustment strategy was maintained. The improved PSO algorithm was applied in the training of radial basis function artificial neural network (RBF ANN) model and the selection of molecular descriptors. A quantitative structure-activity relationship model based on RBF ANN trained by the improved PSO algorithm was proposed to predict the pKa values of 74 kinds of neutral and basic drugs and then validated by another database containing 20 molecules. The validation results showed that the model had a good prediction performance. The absolute average relative error, root mean square error, and squared correlation coefficient were 0.3105, 0.0411, and 0.9685, respectively. The model can be used as a reference for exploring other quantitative structure-activity relationships.
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Affiliation(s)
- Mengshan Li
- College of Physics and Electronic Information, Gannan Normal University, Ganzhou, Jiangxi, 341000, China.
| | - Huaijing Zhang
- College of Physics and Electronic Information, Gannan Normal University, Ganzhou, Jiangxi, 341000, China
| | - Bingsheng Chen
- College of Physics and Electronic Information, Gannan Normal University, Ganzhou, Jiangxi, 341000, China
| | - Yan Wu
- College of Physics and Electronic Information, Gannan Normal University, Ganzhou, Jiangxi, 341000, China
| | - Lixin Guan
- College of Physics and Electronic Information, Gannan Normal University, Ganzhou, Jiangxi, 341000, China
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Zheng M, Zhao J, Cui C, Fu Z, Li X, Liu X, Ding X, Tan X, Li F, Luo X, Chen K, Jiang H. Computational chemical biology and drug design: Facilitating protein structure, function, and modulation studies. Med Res Rev 2018; 38:914-950. [DOI: 10.1002/med.21483] [Citation(s) in RCA: 27] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/15/2017] [Revised: 12/13/2017] [Accepted: 12/15/2017] [Indexed: 12/12/2022]
Affiliation(s)
- Mingyue Zheng
- State Key Laboratory of Drug Research, Drug Discovery and Design Center, Shanghai Institute of Materia Medica; Chinese Academy of Sciences; Shanghai China
| | - Jihui Zhao
- State Key Laboratory of Drug Research, Drug Discovery and Design Center, Shanghai Institute of Materia Medica; Chinese Academy of Sciences; Shanghai China
| | - Chen Cui
- State Key Laboratory of Drug Research, Drug Discovery and Design Center, Shanghai Institute of Materia Medica; Chinese Academy of Sciences; Shanghai China
| | - Zunyun Fu
- State Key Laboratory of Drug Research, Drug Discovery and Design Center, Shanghai Institute of Materia Medica; Chinese Academy of Sciences; Shanghai China
| | - Xutong Li
- State Key Laboratory of Drug Research, Drug Discovery and Design Center, Shanghai Institute of Materia Medica; Chinese Academy of Sciences; Shanghai China
| | - Xiaohong Liu
- State Key Laboratory of Drug Research, Drug Discovery and Design Center, Shanghai Institute of Materia Medica; Chinese Academy of Sciences; Shanghai China
- School of Life Science and Technology; ShanghaiTech University; Shanghai China
| | - Xiaoyu Ding
- State Key Laboratory of Drug Research, Drug Discovery and Design Center, Shanghai Institute of Materia Medica; Chinese Academy of Sciences; Shanghai China
| | - Xiaoqin Tan
- State Key Laboratory of Drug Research, Drug Discovery and Design Center, Shanghai Institute of Materia Medica; Chinese Academy of Sciences; Shanghai China
| | - Fei Li
- State Key Laboratory of Drug Research, Drug Discovery and Design Center, Shanghai Institute of Materia Medica; Chinese Academy of Sciences; Shanghai China
- Department of Chemistry, College of Sciences; Shanghai University; Shanghai China
| | - Xiaomin Luo
- State Key Laboratory of Drug Research, Drug Discovery and Design Center, Shanghai Institute of Materia Medica; Chinese Academy of Sciences; Shanghai China
| | - Kaixian Chen
- State Key Laboratory of Drug Research, Drug Discovery and Design Center, Shanghai Institute of Materia Medica; Chinese Academy of Sciences; Shanghai China
- School of Life Science and Technology; ShanghaiTech University; Shanghai China
| | - Hualiang Jiang
- State Key Laboratory of Drug Research, Drug Discovery and Design Center, Shanghai Institute of Materia Medica; Chinese Academy of Sciences; Shanghai China
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28
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Affiliation(s)
- Saeed Alqahtani
- Department of Clinical Pharmacy, College of Pharmacy, King Saud University, Riyadh, Saudi Arabia
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29
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ADME-Space: a new tool for medicinal chemists to explore ADME properties. Sci Rep 2017; 7:6359. [PMID: 28743970 PMCID: PMC5527008 DOI: 10.1038/s41598-017-06692-0] [Citation(s) in RCA: 42] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/08/2017] [Accepted: 06/15/2017] [Indexed: 01/03/2023] Open
Abstract
We introduce a new chemical space for drugs and drug-like molecules, exclusively based on their in silico ADME behaviour. This ADME-Space is based on self-organizing map (SOM) applied to 26,000 molecules. Twenty accurate QSPR models, describing important ADME properties, were developed and, successively, used as new molecular descriptors not related to molecular structure. Applications include permeability, active transport, metabolism and bioavailability studies, but the method can be even used to discuss drug-drug interactions (DDIs) or it can be extended to additional ADME properties. Thus, the ADME-Space opens a new framework for the multi-parametric data analysis in drug discovery where all ADME behaviours of molecules are condensed in one map: it allows medicinal chemists to simultaneously monitor several ADME properties, to rapidly select optimal ADME profiles, retrieve warning on potential ADME problems and DDIs or select proper in vitro experiments.
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