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Vora LK, Gholap AD, Jetha K, Thakur RRS, Solanki HK, Chavda VP. Artificial Intelligence in Pharmaceutical Technology and Drug Delivery Design. Pharmaceutics 2023; 15:1916. [PMID: 37514102 PMCID: PMC10385763 DOI: 10.3390/pharmaceutics15071916] [Citation(s) in RCA: 50] [Impact Index Per Article: 50.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/06/2023] [Revised: 06/28/2023] [Accepted: 07/04/2023] [Indexed: 07/30/2023] Open
Abstract
Artificial intelligence (AI) has emerged as a powerful tool that harnesses anthropomorphic knowledge and provides expedited solutions to complex challenges. Remarkable advancements in AI technology and machine learning present a transformative opportunity in the drug discovery, formulation, and testing of pharmaceutical dosage forms. By utilizing AI algorithms that analyze extensive biological data, including genomics and proteomics, researchers can identify disease-associated targets and predict their interactions with potential drug candidates. This enables a more efficient and targeted approach to drug discovery, thereby increasing the likelihood of successful drug approvals. Furthermore, AI can contribute to reducing development costs by optimizing research and development processes. Machine learning algorithms assist in experimental design and can predict the pharmacokinetics and toxicity of drug candidates. This capability enables the prioritization and optimization of lead compounds, reducing the need for extensive and costly animal testing. Personalized medicine approaches can be facilitated through AI algorithms that analyze real-world patient data, leading to more effective treatment outcomes and improved patient adherence. This comprehensive review explores the wide-ranging applications of AI in drug discovery, drug delivery dosage form designs, process optimization, testing, and pharmacokinetics/pharmacodynamics (PK/PD) studies. This review provides an overview of various AI-based approaches utilized in pharmaceutical technology, highlighting their benefits and drawbacks. Nevertheless, the continued investment in and exploration of AI in the pharmaceutical industry offer exciting prospects for enhancing drug development processes and patient care.
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Affiliation(s)
- Lalitkumar K Vora
- School of Pharmacy, Queen's University Belfast, 97 Lisburn Road, Belfast BT9 7BL, UK
| | - Amol D Gholap
- Department of Pharmaceutics, St. John Institute of Pharmacy and Research, Palghar 401404, Maharashtra, India
| | - Keshava Jetha
- Department of Pharmaceutics and Pharmaceutical Technology, L. M. College of Pharmacy, Ahmedabad 380009, Gujarat, India
- Ph.D. Section, Gujarat Technological University, Ahmedabad 382424, Gujarat, India
| | | | - Hetvi K Solanki
- Pharmacy Section, L. M. College of Pharmacy, Ahmedabad 380009, Gujarat, India
| | - Vivek P Chavda
- Department of Pharmaceutics and Pharmaceutical Technology, L. M. College of Pharmacy, Ahmedabad 380009, Gujarat, India
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Zhang T, Androulakis IP, Bonate P, Cheng L, Helikar T, Parikh J, Rackauckas C, Subramanian K, Cho CR. Two heads are better than one: current landscape of integrating QSP and machine learning : An ISoP QSP SIG white paper by the working group on the integration of quantitative systems pharmacology and machine learning. J Pharmacokinet Pharmacodyn 2022; 49:5-18. [PMID: 35103884 PMCID: PMC8837505 DOI: 10.1007/s10928-022-09805-z] [Citation(s) in RCA: 20] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/27/2021] [Accepted: 01/10/2022] [Indexed: 12/02/2022]
Abstract
Quantitative systems pharmacology (QSP) modeling is applied to address essential questions in drug development, such as the mechanism of action of a therapeutic agent and the progression of disease. Meanwhile, machine learning (ML) approaches also contribute to answering these questions via the analysis of multi-layer 'omics' data such as gene expression, proteomics, metabolomics, and high-throughput imaging. Furthermore, ML approaches can also be applied to aspects of QSP modeling. Both approaches are powerful tools and there is considerable interest in integrating QSP modeling and ML. So far, a few successful implementations have been carried out from which we have learned about how each approach can overcome unique limitations of the other. The QSP + ML working group of the International Society of Pharmacometrics QSP Special Interest Group was convened in September, 2019 to identify and begin realizing new opportunities in QSP and ML integration. The working group, which comprises 21 members representing 18 academic and industry organizations, has identified four categories of current research activity which will be described herein together with case studies of applications to drug development decision making. The working group also concluded that the integration of QSP and ML is still in its early stages of moving from evaluating available technical tools to building case studies. This paper reports on this fast-moving field and serves as a foundation for future codification of best practices.
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Affiliation(s)
- Tongli Zhang
- University of Cincinnati, Cincinnati, OH, 45267, USA.
| | | | | | | | - Tomáš Helikar
- Department of Biochemistry, University of Nebraska-Lincoln, Lincoln, NE, USA
| | | | - Christopher Rackauckas
- Pumas-AI, Baltimore, MD, USA
- Department of Mathematics, Massachusetts Institute of Technology, Boston, MA, USA
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Terterov IN, Chubenko VA, Knyazev NA, Klimenko VV, Bogdanov AA, Moiseyenko VM, Bogdanov AA. Minimal PK/PD model for simultaneous description of the maximal tolerated dose and metronomic treatment outcomes in mouse tumor models. Cancer Chemother Pharmacol 2021; 88:867-878. [PMID: 34351468 DOI: 10.1007/s00280-021-04326-x] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/04/2021] [Accepted: 07/06/2021] [Indexed: 01/08/2023]
Abstract
PURPOSE Metronomic chemotherapy (MC) is a promising approach where, in contrast to the conventional maximal tolerated dose (MTD) strategy, regular fractionated doses of the drug are used. This approach has proven its efficacy, although drug dosing and scheduling are often chosen empirically. Pharmacokinetic/pharmacodynamic (PK/PD) models provide a way to choose optimal protocols with computational methods. Existing models are usually too complicated and are valid for only a subset of drug schedules. To address this issue, we propose herein a simple model that can describe MC and MTD regimens simultaneously. METHODS The minimal model comprises tumor suppression due to antiangiogenic drug effect together with a cell-kill term, responsible for its cytotoxicity. The model was tested on data obtained on tumor-bearing mice treated with gemcitabine in ether MTD, MC, or combined (MTD + MC) regimens. RESULTS We conducted a number of tests in which data were divided in various ways into training and validation sets. The model successfully described different trends in the MTD and MC regimens. With parameters obtained by fitting the model to MTD data, the simulations correctly predicted trends in both the MC and combined therapy groups. CONCLUSION Our results demonstrate that the proposed model presents a minimal yet efficient tool for modeling outcomes in different treatment regimens in mice. We hope that this model has the potential for use in clinical practice in the development of patient-specific chemotherapy scheduling protocols based on observed treatment response.
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Affiliation(s)
- Ivan N Terterov
- Saint-Petersburg Clinical Scientific and Practical Center of Specialized Types of Medical Care (Oncological), St. Petersburg, Russia.
| | - Vyacheslav A Chubenko
- Saint-Petersburg Clinical Scientific and Practical Center of Specialized Types of Medical Care (Oncological), St. Petersburg, Russia
| | - Nikolay A Knyazev
- Saint-Petersburg Clinical Scientific and Practical Center of Specialized Types of Medical Care (Oncological), St. Petersburg, Russia
| | - Vladimir V Klimenko
- Saint-Petersburg Clinical Scientific and Practical Center of Specialized Types of Medical Care (Oncological), St. Petersburg, Russia
| | - Andrei A Bogdanov
- Saint-Petersburg Clinical Scientific and Practical Center of Specialized Types of Medical Care (Oncological), St. Petersburg, Russia
| | - Vladimir M Moiseyenko
- Saint-Petersburg Clinical Scientific and Practical Center of Specialized Types of Medical Care (Oncological), St. Petersburg, Russia
| | - Alexey A Bogdanov
- Saint-Petersburg Clinical Scientific and Practical Center of Specialized Types of Medical Care (Oncological), St. Petersburg, Russia
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Houy N, Flaig J. Hospital-wide surveillance-based antimicrobial treatments: A Monte-Carlo look-ahead method. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2021; 204:106050. [PMID: 33780890 DOI: 10.1016/j.cmpb.2021.106050] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/24/2020] [Accepted: 03/06/2021] [Indexed: 06/12/2023]
Abstract
BACKGROUND AND OBJECTIVES We present a heuristic solution method to the problem of choosing hospital-wide antimicrobial treatments that minimize the cumulative infected patient-days in the long run in a health care facility. METHODS Our solution method is a rollout algorithm. We rely on the stochastic version of a compartmental model to describe the spread of an infecting organism in the health care facility and the emergence and spread of resistance to two drugs. We assume that the parameters of the model are known. Treatments are chosen at the beginning of each period based on the count of patients with each health status, and on stochastic simulations of the future emergence and spread of antimicrobial resistance. The same treatment is then administered to all patients, including uninfected patients, during the period and cannot be adjusted until the next period. RESULTS In our simulations, our algorithm allows to reduce the average cumulative infected patient-days over two years by 47.0% compared to the best standard therapy, and by 32.2% compared to a similar heuristic algorithm not using surveillance data (significantly at the 95% threshold). CONCLUSION Our heuristic solution method is simple yet flexible. We explain how it can be used either to perform online optimization, or to produce data for quantitative analysis. Its performance is illustrated using a relatively simple infectious disease transmission model, but it is compatible with more advanced epidemiological models.
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Affiliation(s)
- Nicolas Houy
- University of Lyon, Lyon, F-69007, France; CNRS, GATE Lyon Saint-Etienne, F-69130, France.
| | - Julien Flaig
- EPIMOD, Epidemiology and Modelling, Lyon, France.
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Garcia-Rosa S, de Freitas Brenha B, Felipe da Rocha V, Goulart E, Araujo BHS. Personalized Medicine Using Cutting Edge Technologies for Genetic Epilepsies. Curr Neuropharmacol 2021; 19:813-831. [PMID: 32933463 PMCID: PMC8686309 DOI: 10.2174/1570159x18666200915151909] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/09/2020] [Revised: 08/08/2020] [Accepted: 08/28/2020] [Indexed: 11/22/2022] Open
Abstract
Epilepsy is the most common chronic neurologic disorder in the world, affecting 1-2% of the population. Besides, 30% of epilepsy patients are drug-resistant. Genomic mutations seem to play a key role in its etiology and knowledge of strong effect mutations in protein structures might improve prediction and the development of efficacious drugs to treat epilepsy. Several genetic association studies have been undertaken to examine the effect of a range of candidate genes for resistance. Although, few studies have explored the effect of the mutations into protein structure and biophysics in the epilepsy field. Much work remains to be done, but the plans made for exciting developments will hold therapeutic potential for patients with drug-resistance. In summary, we provide a critical review of the perspectives for the development of individualized medicine for epilepsy based on genetic polymorphisms/mutations in light of core elements such as transcriptomics, structural biology, disease model, pharmacogenomics and pharmacokinetics in a manner to improve the success of trial designs of antiepileptic drugs.
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Affiliation(s)
- Sheila Garcia-Rosa
- Brazilian Biosciences National Laboratory (LNBio), Center for Research in Energy and Material (CNPEM), Campinas, SP, Brazil
| | - Bianca de Freitas Brenha
- Laboratory of Embryonic Genetic Regulation, Department of Biochemistry and Tissue Biology, Institute of Biology, University of Campinas, Campinas, SP, Brazil
| | - Vinicius Felipe da Rocha
- Brazilian Biosciences National Laboratory (LNBio), Center for Research in Energy and Material (CNPEM), Campinas, SP, Brazil
| | - Ernesto Goulart
- Human Genome and Stem-Cell Research Center (HUG-CEL), Department of Genetics and Evolutionary Biology, Institute of Biosciences, University of São Paulo, SP, Brazil
| | - Bruno Henrique Silva Araujo
- Brazilian Biosciences National Laboratory (LNBio), Center for Research in Energy and Material (CNPEM), Campinas, SP, Brazil
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Houy N, Flaig J. Optimal dynamic empirical therapy in a health care facility: A Monte-Carlo look-ahead method. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2021; 198:105767. [PMID: 33086150 DOI: 10.1016/j.cmpb.2020.105767] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/24/2019] [Accepted: 09/18/2020] [Indexed: 06/11/2023]
Abstract
BACKGROUND AND OBJECTIVES Empirical antimicrobial prescription strategies have been proposed to counteract the selection of resistant pathogenic strains. The respective merits of such strategies have been debated. Rather than comparing a finite number of policies, we take an optimization approach and propose a solution to the problem of finding an empirical therapy policy in a health care facility that minimizes the cumulative infected patient-days over a given time horizon. METHODS We assume that the parameters of the model are known and that when the policy is implemented, all patients receive the same treatment at a given time. We model the emergence and spread of antimicrobial resistance at the population level with the stochastic version of a compartmental model. The model features two drugs and the possibility of double resistance. Our solution method is a rollout algorithm. RESULTS In our example, the optimal policy computed with this method allows to reduce the average cumulative infected patient-days over two years by 22% compared to the best standard therapy. Considering regularity constraints, we could derive a policy with a fixed period and a performance close to that of the optimal policy. The average cumulative infected patient-days over two years obtained with the optimal policy is 6% lower (significantly at the 95% threshold) than that obtained with the fixed period policy. CONCLUSION Our results illustrate the performance of a highly flexible solution method that will contribute to the development of implementable empirical therapy policies.
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Affiliation(s)
- Nicolas Houy
- University of Lyon, Lyon, F-69007, France; CNRS, GATE Lyon Saint-Etienne, F-69130, France.
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Angehrn Z, Haldna L, Zandvliet AS, Gil Berglund E, Zeeuw J, Amzal B, Cheung SYA, Polasek TM, Pfister M, Kerbusch T, Heckman NM. Artificial Intelligence and Machine Learning Applied at the Point of Care. Front Pharmacol 2020; 11:759. [PMID: 32625083 PMCID: PMC7314939 DOI: 10.3389/fphar.2020.00759] [Citation(s) in RCA: 25] [Impact Index Per Article: 6.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/15/2019] [Accepted: 05/06/2020] [Indexed: 12/17/2022] Open
Abstract
Introduction The increasing availability of healthcare data and rapid development of big data analytic methods has opened new avenues for use of Artificial Intelligence (AI)- and Machine Learning (ML)-based technology in medical practice. However, applications at the point of care are still scarce. Objective Review and discuss case studies to understand current capabilities for applying AI/ML in the healthcare setting, and regulatory requirements in the US, Europe and China. Methods A targeted narrative literature review of AI/ML based digital tools was performed. Scientific publications (identified in PubMed) and grey literature (identified on the websites of regulatory agencies) were reviewed and analyzed. Results From the regulatory perspective, AI/ML-based solutions can be considered medical devices (i.e., Software as Medical Device, SaMD). A case series of SaMD is presented. First, tools for monitoring and remote management of chronic diseases are presented. Second, imaging applications for diagnostic support are discussed. Finally, clinical decision support tools to facilitate the choice of treatment and precision dosing are reviewed. While tested and validated algorithms for precision dosing exist, their implementation at the point of care is limited, and their regulatory and commercialization pathway is not clear. Regulatory requirements depend on the level of risk associated with the use of the device in medical practice, and can be classified into administrative (manufacturing and quality control), software-related (design, specification, hazard analysis, architecture, traceability, software risk analysis, cybersecurity, etc.), clinical evidence (including patient perspectives in some cases), non-clinical evidence (dosing validation and biocompatibility/toxicology) and other, such as e.g. benefit-to-risk determination, risk assessment and mitigation. There generally is an alignment between the US and Europe. China additionally requires that the clinical evidence is applicable to the Chinese population and recommends that a third-party central laboratory evaluates the clinical trial results. Conclusions The number of promising AI/ML-based technologies is increasing, but few have been implemented widely at the point of care. The need for external validation, implementation logistics, and data exchange and privacy remain the main obstacles.
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Affiliation(s)
| | | | | | | | | | | | | | - Thomas M Polasek
- Certara, Princeton, NJ, United States.,Department of Clinical Pharmacology, Royal Adelaide Hospital, Adelaide, SA, Australia.,Centre for Medicines Use and Safety, Monash University, Melbourne, VIC, Australia
| | - Marc Pfister
- Certara, Princeton, NJ, United States.,Department of Pharmacology and Pharmacometrics, Children's University Hospital Basel, Basel, Switzerland
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Murphy H, McCarthy G, Dobrovolny HM. Understanding the effect of measurement time on drug characterization. PLoS One 2020; 15:e0233031. [PMID: 32407356 PMCID: PMC7224495 DOI: 10.1371/journal.pone.0233031] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/30/2020] [Accepted: 04/27/2020] [Indexed: 12/24/2022] Open
Abstract
In order to determine correct dosage of chemotherapy drugs, the effect of the drug must be properly quantified. There are two important values that characterize the effect of the drug: εmax is the maximum possible effect of a drug, and IC50 is the drug concentration where the effect diminishes by half. There is currently a problem with the way these values are measured because they are time-dependent measurements. We use mathematical models to determine how the εmax and IC50 values depend on measurement time and model choice. Seven ordinary differential equation models (ODE) are used for the mathematical analysis; the exponential, Mendelsohn, logistic, linear, surface, Bertalanffy, and Gompertz models. We use the models to simulate tumor growth in the presence and absence of treatment with a known IC50 and εmax. Using traditional methods, we then calculate the IC50 and εmax values over fifty days to show the time-dependence of these values for all seven mathematical models. The general trend found is that the measured IC50 value decreases and the measured εmax increases with increasing measurement day for most mathematical models. Unfortunately, the measured values of IC50 and εmax rarely matched the values used to generate the data. Our results show that there is no optimal measurement time since models predict that IC50 estimates become more accurate at later measurement times while εmax is more accurate at early measurement times.
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Affiliation(s)
- Hope Murphy
- Department of Physics & Astronomy, Texas Christian University, Fort Worth, TX, United States of America
| | - Gabriel McCarthy
- Department of Physics & Astronomy, Texas Christian University, Fort Worth, TX, United States of America
| | - Hana M. Dobrovolny
- Department of Physics & Astronomy, Texas Christian University, Fort Worth, TX, United States of America
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Houy N, Le Grand F. Personalized oncology with artificial intelligence: The case of temozolomide. Artif Intell Med 2019; 99:101693. [DOI: 10.1016/j.artmed.2019.07.001] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/01/2018] [Revised: 07/09/2019] [Accepted: 07/09/2019] [Indexed: 10/26/2022]
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Abstract
The most common applications of artificial intelligence (AI) in drug treatment have to do with matching patients to their optimal drug or combination of drugs, predicting drug-target or drug-drug interactions, and optimizing treatment protocols. This review outlines some of the recently developed AI methods aiding the drug treatment and administration process. Selection of the best drug(s) for a patient typically requires the integration of patient data, such as genetics or proteomics, with drug data, like compound chemical descriptors, to score the therapeutic efficacy of drugs. The prediction of drug interactions often relies on similarity metrics, assuming that drugs with similar structures or targets will have comparable behavior or may interfere with each other. Optimizing the dosage schedule for administration of drugs is performed using mathematical models to interpret pharmacokinetic and pharmacodynamic data. The recently developed and powerful models for each of these tasks are addressed, explained, and analyzed here.
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Affiliation(s)
- Eden L Romm
- CureMatch Inc., San Diego, California 92121, USA
| | - Igor F Tsigelny
- CureMatch Inc., San Diego, California 92121, USA.,San Diego Supercomputer Center, University of California, San Diego, La Jolla, California 92093, USA;
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Chamseddine IM, Rejniak KA. Hybrid modeling frameworks of tumor development and treatment. WILEY INTERDISCIPLINARY REVIEWS-SYSTEMS BIOLOGY AND MEDICINE 2019; 12:e1461. [PMID: 31313504 PMCID: PMC6898741 DOI: 10.1002/wsbm.1461] [Citation(s) in RCA: 34] [Impact Index Per Article: 6.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 04/29/2019] [Revised: 06/13/2019] [Accepted: 06/13/2019] [Indexed: 12/15/2022]
Abstract
Tumors are complex multicellular heterogeneous systems comprised of components that interact with and modify one another. Tumor development depends on multiple factors: intrinsic, such as genetic mutations, altered signaling pathways, or variable receptor expression; and extrinsic, such as differences in nutrient supply, crosstalk with stromal or immune cells, or variable composition of the surrounding extracellular matrix. Tumors are also characterized by high cellular heterogeneity and dynamically changing tumor microenvironments. The complexity increases when this multiscale, multicomponent system is perturbed by anticancer treatments. Modeling such complex systems and predicting how tumors will respond to therapies require mathematical models that can handle various types of information and combine diverse theoretical methods on multiple temporal and spatial scales, that is, hybrid models. In this update, we discuss the progress that has been achieved during the last 10 years in the area of the hybrid modeling of tumors. The classical definition of hybrid models refers to the coupling of discrete descriptions of cells with continuous descriptions of microenvironmental factors. To reflect on the direction that the modeling field has taken, we propose extending the definition of hybrid models to include of coupling two or more different mathematical frameworks. Thus, in addition to discussing recent advances in discrete/continuous modeling, we also discuss how these two mathematical descriptions can be coupled with theoretical frameworks of optimal control, optimization, fluid dynamics, game theory, and machine learning. All these methods will be illustrated with applications to tumor development and various anticancer treatments. This article is characterized under:Analytical and Computational Methods > Computational Methods Translational, Genomic, and Systems Medicine > Therapeutic Methods Models of Systems Properties and Processes > Organ, Tissue, and Physiological Models
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Affiliation(s)
- Ibrahim M. Chamseddine
- Department of Integrated Mathematical OncologyH. Lee Moffitt Cancer Center and Research InstituteTampaFlorida
| | - Katarzyna A. Rejniak
- Department of Integrated Mathematical OncologyH. Lee Moffitt Cancer Center and Research InstituteTampaFlorida
- Department of Oncologic Sciences, Morsani College of MedicineUniversity of South FloridaTampaFlorida
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Houy N, Le Grand F. Optimizing treatment combination for lymphoma using an optimization heuristic. Math Biosci 2019; 315:108227. [PMID: 31302209 DOI: 10.1016/j.mbs.2019.108227] [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: 09/17/2018] [Revised: 07/09/2019] [Accepted: 07/10/2019] [Indexed: 10/26/2022]
Abstract
BACKGROUND The standard treatment for high-grade non-Hodgkin lymphoma involves the combination of chemotherapy and immunotherapy. We characterize in-silico the optimal combination protocol that maximizes the overall survival probability. We rely on a pharmacokinetics/pharmacodynamics (PK/PD) model that describes the joint evolution of tumor and effector cells, as well as the effects of both chemotherapy and immunotherapy. The toxicity is taken into account through ad-hoc constraints. We develop an optimization algorithm that belongs to the class of Monte-Carlo tree search algorithms. Our simulations rely on an in-silico population of heterogeneous patients differing with respect to their PK/PD parameters. The optimization objective consists in characterizing the combination protocol that maximizes the overall survival probability of the patient population under consideration. RESULTS We compare using in-silico experiments our results to standard protocols and observe a gain in overall survival probabilities that vary from 4 to 9 percentage points. The gains increase with the complexity of the potential protocol. Gains are larger in presence of a higher number of injections or of an actual combination with immunotherapy. CONCLUSIONS In in-silico experiments, optimal protocols achieve significant gains over standard protocols when considering overall survival probabilities. Our optimization algorithm enables us to efficiently tackle this numerical problem with a large dimensionality. The in-vivo implications of our in-silico results remain to be explored.
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Affiliation(s)
- Nicolas Houy
- University of Lyon, Lyon F-69007, France; CNRS, GATE Lyon Saint-Etienne, F-69130, France.
| | - François Le Grand
- emlyon business school, Écully F-69130, France; ETH Zurich, Zurich CH-8092, Switzerland
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Optimizing immune cell therapies with artificial intelligence. J Theor Biol 2019; 461:34-40. [DOI: 10.1016/j.jtbi.2018.09.007] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/19/2018] [Revised: 09/04/2018] [Accepted: 09/07/2018] [Indexed: 11/22/2022]
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