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Terranova N, Renard D, Shahin MH, Menon S, Cao Y, Hop CECA, Hayes S, Madrasi K, Stodtmann S, Tensfeldt T, Vaddady P, Ellinwood N, Lu J. Artificial Intelligence for Quantitative Modeling in Drug Discovery and Development: An Innovation and Quality Consortium Perspective on Use Cases and Best Practices. Clin Pharmacol Ther 2024; 115:658-672. [PMID: 37716910 DOI: 10.1002/cpt.3053] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/28/2023] [Accepted: 09/11/2023] [Indexed: 09/18/2023]
Abstract
Recent breakthroughs in artificial intelligence (AI) and machine learning (ML) have ushered in a new era of possibilities across various scientific domains. One area where these advancements hold significant promise is model-informed drug discovery and development (MID3). To foster a wider adoption and acceptance of these advanced algorithms, the Innovation and Quality (IQ) Consortium initiated the AI/ML working group in 2021 with the aim of promoting their acceptance among the broader scientific community as well as by regulatory agencies. By drawing insights from workshops organized by the working group and attended by key stakeholders across the biopharma industry, academia, and regulatory agencies, this white paper provides a perspective from the IQ Consortium. The range of applications covered in this white paper encompass the following thematic topics: (i) AI/ML-enabled Analytics for Pharmacometrics and Quantitative Systems Pharmacology (QSP) Workflows; (ii) Explainable Artificial Intelligence and its Applications in Disease Progression Modeling; (iii) Natural Language Processing (NLP) in Quantitative Pharmacology Modeling; and (iv) AI/ML Utilization in Drug Discovery. Additionally, the paper offers a set of best practices to ensure an effective and responsible use of AI, including considering the context of use, explainability and generalizability of models, and having human-in-the-loop. We believe that embracing the transformative power of AI in quantitative modeling while adopting a set of good practices can unlock new opportunities for innovation, increase efficiency, and ultimately bring benefits to patients.
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Affiliation(s)
- Nadia Terranova
- Quantitative Pharmacology, Merck KGaA, Lausanne, Switzerland
| | - Didier Renard
- Full Development Pharmacometrics, Novartis Pharma AG, Basel, Switzerland
| | | | - Sujatha Menon
- Clinical Pharmacology, Pfizer Inc., Groton, Connecticut, USA
| | - Youfang Cao
- Clinical Pharmacology and Translational Medicine, Eisai Inc., Nutley, New Jersey, USA
| | | | - Sean Hayes
- Quantitative Pharmacology & Pharmacometrics, Merck & Co. Inc., Rahway, New Jersey, USA
| | - Kumpal Madrasi
- Modeling & Simulation, Sanofi, Bridgewater, New Jersey, USA
| | - Sven Stodtmann
- Pharmacometrics, AbbVie Deutschland GmbH & Co. KG, Ludwigshafen, Germany
| | | | - Pavan Vaddady
- Quantitative Clinical Pharmacology, Daiichi Sankyo, Inc., Basking Ridge, New Jersey, USA
| | | | - James Lu
- Clinical Pharmacology, Genentech Inc., South San Francisco, California, USA
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Pharmacometrics in tuberculosis: progress and opportunities. Int J Antimicrob Agents 2022; 60:106620. [PMID: 35724859 DOI: 10.1016/j.ijantimicag.2022.106620] [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: 03/03/2022] [Revised: 05/23/2022] [Accepted: 06/12/2022] [Indexed: 11/22/2022]
Abstract
Tuberculosis remains one of the leading causes of death by a communicable agent, infecting up to one-quarter of the world's population, predominantly in disadvantaged communities. Pharmacometrics employs quantitative mathematical models to describe the relationships between pharmacokinetics and pharmacodynamics, and to predict drug doses, exposures, and responses. Pharmacometric approaches have provided a scientific basis for improved dosing of antituberculosis drugs and concomitantly administered antiretrovirals at the population level. The development of modelling frameworks including physiologically-based pharmacokinetics, quantitative systems pharmacology and machine learning provides an opportunity to extend the role of pharmacometrics to in silico quantification of drug-drug interactions, prediction of doses for special populations, dose optimization and individualization, and understanding the complex exposure-response relationships of multidrug regimens in terms of both efficacy and safety, informing regimen design for future study. In this short clinically-focused review, we explore what has been done, and what opportunities exist for pharmacometrics to impact tuberculosis pharmacotherapy.
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Soeorg H, Sverrisdóttir E, Andersen M, Lund TM, Sessa M. The PHARMACOM-EPI Framework for Integrating Pharmacometric Modelling Into Pharmacoepidemiological Research Using Real-World Data: Application to Assess Death Associated With Valproate. Clin Pharmacol Ther 2021; 111:840-856. [PMID: 34860420 DOI: 10.1002/cpt.2502] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/21/2021] [Accepted: 11/17/2021] [Indexed: 01/14/2023]
Abstract
In pharmacoepidemiology, it is usually expected that the observed association should be directly or indirectly related to the pharmacological effects of the drug/s under investigation. Pharmacological effects are, in turn, strongly connected to the pharmacokinetic and pharmacodynamic properties of a drug, which can be characterized and investigated using pharmacometric models. Recently, the use of pharmacometrics has been proposed to provide pharmacological substantiation of pharmacoepidemiological findings derived from real-world data. However, validated frameworks suggesting how to combine these two disciplines for the aforementioned purpose are missing. Therefore, we propose PHARMACOM-EPI, a framework that provides a structured approach on how to identify, characterize, and apply pharmacometric models with practical details on how to choose software, format dataset, handle missing covariates/dosing data, how to perform the external evaluation of pharmacometric models in real-world data, and how to provide pharmacological substantiation of pharmacoepidemiological findings. PHARMACOM-EPI was tested in a proof-of-concept study to pharmacologically substantiate death associated with valproate use in the Danish population aged ≥ 65 years. Pharmacological substantiation of death during a follow-up period of 1 year showed that in all individuals who died (n = 169) individual predictions were within the subtherapeutic range compared with 52.8% of those who did not die (n = 1,084). Of individuals who died, 66.3% (n = 112) had a cause of death possibly related to valproate and 33.7% (n = 57) with well-defined cause of death unlikely related to valproate. This proof-of-concept study showed that PHARMACOM-EPI was able to provide pharmacological substantiation for death associated with valproate use in the study population.
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Affiliation(s)
- Hiie Soeorg
- Department of Drug Design and Pharmacology, Pharmacovigilance Research Center, University of Copenhagen, Copenhagen, Denmark.,Department of Drug Design and Pharmacology, Pharmacometrics Research Group, University of Copenhagen, Copenhagen, Denmark
| | - Eva Sverrisdóttir
- Department of Drug Design and Pharmacology, Pharmacometrics Research Group, University of Copenhagen, Copenhagen, Denmark
| | - Morten Andersen
- Department of Drug Design and Pharmacology, Pharmacovigilance Research Center, University of Copenhagen, Copenhagen, Denmark
| | - Trine Meldgaard Lund
- Department of Drug Design and Pharmacology, Pharmacometrics Research Group, University of Copenhagen, Copenhagen, Denmark
| | - Maurizio Sessa
- Department of Drug Design and Pharmacology, Pharmacovigilance Research Center, University of Copenhagen, Copenhagen, Denmark
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McIntosh A, Sverdlov O, Yu L, Kaufmann P. Clinical Design and Analysis Strategies for the Development of Gene Therapies: Considerations for Quantitative Drug Development in the Age of Genetic Medicine. Clin Pharmacol Ther 2021; 110:1207-1215. [PMID: 33666225 DOI: 10.1002/cpt.2224] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/19/2021] [Accepted: 03/01/2021] [Indexed: 12/19/2022]
Abstract
Cell and gene therapies have shown enormous promise across a range of diseases in recent years. Numerous adoptive cell therapy modalities as well as systemic and direct-to-target tissue gene transfer administrations are currently in clinical development. The clinical trial design, development, reporting, and analysis of novel cell and gene therapies can differ significantly from established practices for small molecule drugs and biologics. Here, we discuss important quantitative considerations and key competencies for drug developers in preclinical requirements, trial design, and lifecycle planning for gene therapies. We argue that the unique development path of gene therapies requires practicing quantitative drug developers-statisticians, pharmacometricians, pharmacokineticists, epidemiologists, and medical and translational science leads-to exercise active collaboration and cross-functional learning across development stages.
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Affiliation(s)
| | | | - Li Yu
- Novartis Gene Therapies, Bannockburn, Illinois, USA
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