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Oishi M, Sayama H, Toshimoto K, Nakayama T, Nagasaka Y. Practical QSP application from the preclinical phase to enhance the probability of clinical success: Insights from case studies in oncology. Drug Metab Pharmacokinet 2024; 56:101020. [PMID: 38797089 DOI: 10.1016/j.dmpk.2024.101020] [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: 11/06/2023] [Revised: 02/02/2024] [Accepted: 05/06/2024] [Indexed: 05/29/2024]
Abstract
Quantitative Systems Pharmacology (QSP) has emerged as a promising modeling and simulation (M&S) approach in drug development, with potential to improve clinical success rates. While conventional M&S has significantly contributed to quantitative understanding in late preclinical and clinical phases, it falls short in explaining unexpected phenomena and testing hypotheses in the early research phase. QSP presents a solution to these limitations. To harness the full potential of QSP in early preclinical stages, preclinical modelers who are familiar with conventional M&S need to update their understanding of the differences between conventional M&S and QSP. This review focuses on QSP applications during the preclinical stage, citing case examples and sharing our experiences in oncology. We emphasize the critical role of QSP in increasing the probability of success for clinical proof of concept (PoC) when applied from the early preclinical stage. Enhancing the quality of both hypotheses and QSP models from early preclinical stage is of critical importance. Once a QSP model achieves credibility, it facilitates predictions of clinical responses and potential biomarkers. We propose that sequential QSP applications from preclinical stages can improve success rates of clinical PoC, and emphasize the importance of refining both hypotheses and QSP models throughout the process.
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Affiliation(s)
- Masayo Oishi
- Systems Pharmacology, Non-Clinical Biomedical Science, Applied Research & Operations, Astellas Pharma Inc., Tsukuba, Ibaraki, 305-8585, Japan.
| | - Hiroyuki Sayama
- Systems Pharmacology, Non-Clinical Biomedical Science, Applied Research & Operations, Astellas Pharma Inc., Tsukuba, Ibaraki, 305-8585, Japan
| | - Kota Toshimoto
- Systems Pharmacology, Non-Clinical Biomedical Science, Applied Research & Operations, Astellas Pharma Inc., Tsukuba, Ibaraki, 305-8585, Japan
| | - Takeshi Nakayama
- Systems Pharmacology, Non-Clinical Biomedical Science, Applied Research & Operations, Astellas Pharma Inc., Tsukuba, Ibaraki, 305-8585, Japan
| | - Yasuhisa Nagasaka
- Non-Clinical Biomedical Science, Applied Research & Operations, Astellas Pharma Inc., Tsukuba, Ibaraki, 305-8585, Japan
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Gao W, Liu J, Shtylla B, Venkatakrishnan K, Yin D, Shah M, Nicholas T, Cao Y. Realizing the promise of Project Optimus: Challenges and emerging opportunities for dose optimization in oncology drug development. CPT Pharmacometrics Syst Pharmacol 2024; 13:691-709. [PMID: 37969061 PMCID: PMC11098159 DOI: 10.1002/psp4.13079] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/11/2023] [Revised: 10/20/2023] [Accepted: 10/30/2023] [Indexed: 11/17/2023] Open
Abstract
Project Optimus is a US Food and Drug Administration Oncology Center of Excellence initiative aimed at reforming the dose selection and optimization paradigm in oncology drug development. This project seeks to bring together pharmaceutical companies, international regulatory agencies, academic institutions, patient advocates, and other stakeholders. Although there is much promise in this initiative, there are several challenges that need to be addressed, including multidimensionality of the dose optimization problem in oncology, the heterogeneity of cancer and patients, importance of evaluating long-term tolerability beyond dose-limiting toxicities, and the lack of reliable biomarkers for long-term efficacy. Through the lens of Totality of Evidence and with the mindset of model-informed drug development, we offer insights into dose optimization by building a quantitative knowledge base integrating diverse sources of data and leveraging quantitative modeling tools to build evidence for drug dosage considering exposure, disease biology, efficacy, toxicity, and patient factors. We believe that rational dose optimization can be achieved in oncology drug development, improving patient outcomes by maximizing therapeutic benefit while minimizing toxicity.
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Affiliation(s)
- Wei Gao
- Quantitative PharmacologyEMD Serono Research & Development Institute, Inc.BillericaMassachusettsUSA
| | - Jiang Liu
- Food and Drug AdministrationSilver SpringMarylandUSA
| | - Blerta Shtylla
- Quantitative Systems PharmacologyPfizerSan DiegoCaliforniaUSA
| | - Karthik Venkatakrishnan
- Quantitative PharmacologyEMD Serono Research & Development Institute, Inc.BillericaMassachusettsUSA
| | - Donghua Yin
- Clinical PharmacologyPfizerSan DiegoCaliforniaUSA
| | - Mirat Shah
- Food and Drug AdministrationSilver SpringMarylandUSA
| | | | - Yanguang Cao
- Division of Pharmacotherapy and Experimental Therapeutics, Eshelman School of PharmacyUniversity of North Carolina at Chapel HillChapel HillNorth CarolinaUSA
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Cheng Y, Straube R, Alnaif AE, Huang L, Leil TA, Schmidt BJ. Virtual Populations for Quantitative Systems Pharmacology Models. METHODS IN MOLECULAR BIOLOGY (CLIFTON, N.J.) 2022; 2486:129-179. [PMID: 35437722 DOI: 10.1007/978-1-0716-2265-0_8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Subscribe] [Scholar Register] [Indexed: 10/18/2022]
Abstract
Quantitative systems pharmacology (QSP) places an emphasis on dynamic systems modeling, incorporating considerations from systems biology modeling and pharmacodynamics. The goal of QSP is often to quantitatively predict the effects of clinical therapeutics, their combinations, and their doses on clinical biomarkers and endpoints. In order to achieve this goal, strategies for incorporating clinical data into model calibration are critical. Virtual population (VPop) approaches facilitate model calibration while faced with challenges encountered in QSP model application, including modeling a breadth of clinical therapies, biomarkers, endpoints, utilizing data of varying structure and source, capturing observed clinical variability, and simulating with models that may require more substantial computational time and resources than often found in pharmacometrics applications. VPops are frequently developed in a process that may involve parameterization of isolated pathway models, integration into a larger QSP model, incorporation of clinical data, calibration, and quantitative validation that the model with the accompanying, calibrated VPop is suitable to address the intended question or help with the intended decision. Here, we introduce previous strategies for developing VPops in the context of a variety of therapeutic and safety areas: metabolic disorders, drug-induced liver injury, autoimmune diseases, and cancer. We introduce methodological considerations, prior work for sensitivity analysis and VPop algorithm design, and potential areas for future advancement. Finally, we give a more detailed application example of a VPop calibration algorithm that illustrates recent progress and many of the methodological considerations. In conclusion, although methodologies have varied, VPop strategies have been successfully applied to give valid clinical insights and predictions with the assistance of carefully defined and designed calibration and validation strategies. While a uniform VPop approach for all potential QSP applications may be challenging given the heterogeneity in use considerations, we anticipate continued innovation will help to drive VPop application for more challenging cases of greater scale while developing new rigorous methodologies and metrics.
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Affiliation(s)
- Yougan Cheng
- QSP and PBPK, Bristol Myers Squibb, Princeton, NJ, USA.,Daiichi Sankyo, Inc., Pennington, NJ, USA
| | - Ronny Straube
- QSP and PBPK, Bristol Myers Squibb, Princeton, NJ, USA
| | - Abed E Alnaif
- QSP and PBPK, Bristol Myers Squibb, Princeton, NJ, USA.,EMD Serono, Billerica, MA, USA
| | - Lu Huang
- QSP and PBPK, Bristol Myers Squibb, Princeton, NJ, USA
| | - Tarek A Leil
- QSP and PBPK, Bristol Myers Squibb, Princeton, NJ, USA.,Daiichi Sankyo, Inc., Pennington, NJ, USA
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Zineh I. Quantitative Systems Pharmacology: A Regulatory Perspective on Translation. CPT-PHARMACOMETRICS & SYSTEMS PHARMACOLOGY 2019; 8:336-339. [PMID: 30924594 PMCID: PMC6618141 DOI: 10.1002/psp4.12403] [Citation(s) in RCA: 32] [Impact Index Per Article: 6.4] [Reference Citation Analysis] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 01/30/2019] [Accepted: 03/07/2019] [Indexed: 12/16/2022]
Affiliation(s)
- Issam Zineh
- Office of Clinical Pharmacology, Office of Translational Sciences, Center for Drug Evaluation and Research, US Food and Drug Administration, Silver Spring, Maryland, USA
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Cheng Y, Thalhauser CJ, Smithline S, Pagidala J, Miladinov M, Vezina HE, Gupta M, Leil TA, Schmidt BJ. QSP Toolbox: Computational Implementation of Integrated Workflow Components for Deploying Multi-Scale Mechanistic Models. AAPS JOURNAL 2017; 19:1002-1016. [PMID: 28540623 DOI: 10.1208/s12248-017-0100-x] [Citation(s) in RCA: 39] [Impact Index Per Article: 5.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/05/2017] [Accepted: 05/08/2017] [Indexed: 01/09/2023]
Abstract
Quantitative systems pharmacology (QSP) modeling has become increasingly important in pharmaceutical research and development, and is a powerful tool to gain mechanistic insights into the complex dynamics of biological systems in response to drug treatment. However, even once a suitable mathematical framework to describe the pathophysiology and mechanisms of interest is established, final model calibration and the exploration of variability can be challenging and time consuming. QSP models are often formulated as multi-scale, multi-compartment nonlinear systems of ordinary differential equations. Commonly accepted modeling strategies, workflows, and tools have promise to greatly improve the efficiency of QSP methods and improve productivity. In this paper, we present the QSP Toolbox, a set of functions, structure array conventions, and class definitions that computationally implement critical elements of QSP workflows including data integration, model calibration, and variability exploration. We present the application of the toolbox to an ordinary differential equations-based model for antibody drug conjugates. As opposed to a single stepwise reference model calibration, the toolbox also facilitates simultaneous parameter optimization and variation across multiple in vitro, in vivo, and clinical assays to more comprehensively generate alternate mechanistic hypotheses that are in quantitative agreement with available data. The toolbox also includes scripts for developing and applying virtual populations to mechanistic exploration of biomarkers and efficacy. We anticipate that the QSP Toolbox will be a useful resource that will facilitate implementation, evaluation, and sharing of new methodologies in a common framework that will greatly benefit the community.
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Affiliation(s)
- Yougan Cheng
- Bristol-Myers Squibb, PO Box 4000, Princeton, New Jersey, 08543-4000, USA
| | - Craig J Thalhauser
- Bristol-Myers Squibb, PO Box 4000, Princeton, New Jersey, 08543-4000, USA
| | - Shepard Smithline
- Bristol-Myers Squibb, PO Box 4000, Princeton, New Jersey, 08543-4000, USA
| | - Jyotsna Pagidala
- Bristol-Myers Squibb, PO Box 4000, Princeton, New Jersey, 08543-4000, USA
| | - Marko Miladinov
- Bristol-Myers Squibb, PO Box 4000, Princeton, New Jersey, 08543-4000, USA
| | - Heather E Vezina
- Bristol-Myers Squibb, PO Box 4000, Princeton, New Jersey, 08543-4000, USA
| | - Manish Gupta
- Bristol-Myers Squibb, PO Box 4000, Princeton, New Jersey, 08543-4000, USA
| | - Tarek A Leil
- Bristol-Myers Squibb, PO Box 4000, Princeton, New Jersey, 08543-4000, USA
| | - Brian J Schmidt
- Bristol-Myers Squibb, PO Box 4000, Princeton, New Jersey, 08543-4000, USA.
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