Following this process, we generated 2500 parameter sets with a range of values that give rise to model simulations that are plausible. 5 Virtual populations are not a panacea for dealing with the ...uncertainty in QSP models. ...we would like to emphasize that the generation of virtual patients is not the end‐goal of QSP modeling, any more so than putting up a board is the end goal of carpentry. The synthesis of prior biology knowledge brings uncertainty, but also permits assessment of questions that are not amenable to minimal modeling, such as: evaluating novel combination therapies, extrapolation to longer trials, new indications, and identifying key areas of uncertainty for experiments.
The specific pathways, timescales, and dynamics driving the progression of fibrosis in NAFLD and NASH are not yet fully understood. Hence, a mechanistic model of the pathogenesis and treatment of ...fibrosis in NASH will necessarily have significant uncertainties. The rate of fibrosis progression and the heterogeneity of pathogenesis across patients are not thoroughly quantified. To address this problem, we have developed a continuous-time Markov chain model that is able to capture the heterogeneity of fibrosis progression observed in the clinic. We estimated the average time of disease progression through various stages of fibrosis using seven published clinical studies involving paired liver biopsies. Sensitivity analysis revealed therapeutic intervention at stage F1 or stage F2 results in greatest potential improvement in the average fibrosis scores for a typical patient cohort distribution. These results were in good agreement with a retrospective analysis of placebo-controlled pioglitazone clinical trials for the treatment of NAFLD and NASH. This model provides support for determining patient populations, duration, and potential successful endpoints for clinical trial design in the area of NAFLD and NASH.
The purpose of this work is to develop a mathematical model of energy balance and body weight regulation that can predict species-specific response to common pre-clinical interventions. To this end, ...we evaluate the ability of a previously published mathematical model of mouse metabolism to describe changes in body weight and body composition in rats in response to two short-term interventions. First, we adapt the model to describe body weight and composition changes in Sprague-Dawley rats by fitting to data previously collected from a 26-day caloric restriction study. The calibrated model is subsequently used to describe changes in rat body weight and composition in a 23-day cannabinoid receptor 1 antagonist (CB1Ra) study. While the model describes body weight data well, it fails to replicate body composition changes with CB1Ra treatment. Evaluation of a key model assumption about deposition of fat and fat-free masses shows a limitation of the model in short-term studies due to the constraint placed on the relative change in body composition components. We demonstrate that the model can be modified to overcome this limitation, and propose additional measurements to further test the proposed model predictions. These findings illustrate how mathematical models can be used to support drug discovery and development by identifying key knowledge gaps and aiding in the design of additional experiments to further our understanding of disease-relevant and species-specific physiology.
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DOBA, IZUM, KILJ, NUK, PILJ, PNG, SAZU, SIK, UILJ, UKNU, UL, UM, UPUK
Quantitative translational medicine (QTM) is envisioned as a multifaceted discipline that will galvanize the path from idea to medicine through quantitative translation across the discovery, ...development, regulatory, and utilization spectrum. Here, we summarize results of an American Society for Clinical Pharmacology and Therapeutics (ASCPT) survey on barriers relevant to the advancement of QTM and propose opportunities for its deployment. Importantly, we offer a call to action to break down these barriers through patient‐centered stewardship, effective communication, cross‐sector collaboration, and a modernized educational curriculum.
The pharmaceutical industry is actively applying quantitative systems pharmacology (QSP) to make internal decisions and guide drug development. To facilitate the eventual development of a common ...framework for assessing the credibility of QSP models for clinical drug development, scientists from US Food and Drug Administration and the pharmaceutical industry organized a full-day virtual Scientific Exchange on July 1, 2020. An assessment form was used to ensure consistency in the evaluation process. Among the cases presented, QSP was applied to various therapeutic areas. Applications mostly focused on phase 2 dose selection. Model transparency, including details on expert knowledge and data used for model development, was identified as a major factor for robust model assessment. The case studies demonstrated some commonalities in the workflow of QSP model development, calibration, and validation but differ in the size, scope, and complexity of QSP models, in the acceptance criteria for model calibration and validation, and in the algorithms/approaches used for creating virtual patient populations. Though efforts are being made to build the credibility of QSP models and the confidence is increasing in applying QSP for internal decisions at the clinical stages of drug development, there are still many challenges facing QSP application to late stage drug development. The QSP community needs a strategic plan that includes the ability and flexibility to Adapt, to establish Common expectations for model Credibility needed to inform drug Labeling and patient care, and to AIM to achieve the goal (ACCLAIM).
Graphical abstract
Quantitative Systems Pharmacology (QSP) is an emerging science with increasing application to pharmaceutical research and development paradigms. Through case study we provide an overview of the ...benefits and challenges of applying QSP approaches to inform program decisions in the early stages of drug discovery and development. Specifically, we describe the use of a type 2 diabetes systems model to inform a No-Go decision prior to lead development for a potential GLP-1/GIP dual agonist program, enabling prioritization of exploratory programs with higher probability of clinical success.
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Quantitative systems pharmacology (QSP) is a rapidly emerging discipline with application across a spectrum of challenges facing the pharmaceutical industry, including mechanistically informed ...prioritization of target pathways and combinations in discovery, target population, and dose expansion decisions early in clinical development, and analyses for regulatory authorities late in clinical development. QSP's development has influences from physiologic modeling, systems biology, physiologically‐based pharmacokinetic modeling, and pharmacometrics. Given a varied scientific heritage, a variety of tools to accomplish the demands of model development, application, and model‐based analysis of available data have been developed. We report the outcome from a community survey and resulting analysis of how modelers view the impact and growth of QSP, how they utilize existing tools, and capabilities they need improved to further accelerate their impact on drug development. These results serve as a benchmark and roadmap for advancements to the QSP tool set.
Scientific knowledge and technical skills A solid foundation of scientific knowledge (e.g., basic pharmacokinetic/pharmacodynamic PK/PD and pharmacology concepts) and technical pharmacometrics skills ...is the basis for being able to successfully apply MID3 approaches. 6 Foundational technical skills include but are not limited to nonlinear mixed effects (NLME) PK/PD modeling, mechanistic PK/PD modeling, including physiologically-based pharmacokinetic (PBPK), quantitative systems pharmacology (QSP) modeling and clinical trial simulations, as shown in Table 1. Skill set Content Teaching approach Technical skills ADME principles and NLME modeling ADME principles PK, PK/PD, and disease progression models Statistical principles of NLME modeling Model building and selection approaches Covariate selection approaches Model evaluation and validation approaches Categorical PD variables including dropout models Learn specialized NLME and data management software, such as R Series of courses of increasing difficulty in statistics, medicine, biology, pharmacology, and pharmacometrics Lectures and assignments to acquire software skills facilitated by pharmacometrics faculty Internships at regulatory agencies and/or pharmaceutical industry Mechanistic PK and PD models including PBPK and QSP Molecular pathophysiology and pharmacology Mechanistic PK and PD models including PBPK and QSP Learn specialized PBPK and QSP software Series of courses with increasing difficulty Seminar series in medicine, biology, pharmacology Lectures and assignments to acquire software skills facilitated by pharmacometrics faculty Internships at regulatory agencies and/or pharmaceutical industry Clinical trial simulation Statistical principles underpinning various trial designs Simulating outcomes and operating characteristics (e.g., probability of correct decision) of prospective experiments and clinical trials including variability and uncertainty Introduction to Bayesian PK/PD modeling Perform simulation in R with available add-on packages and learn specialized software One semester course that builds on NLME and mechanistic modeling teaching blocks One semester course on design and statistical analysis of clinical trials Lectures and assignments to acquire software skills facilitated by pharmacometrics faculty Internships at regulatory agencies or pharmaceutical industry (pharmacometrics or statistics department) Introduction to emerging sciences and technologies Novel treatment modalities Novel data source and data modalities Quantitative genetics Machine learning in drug development Seminar series developed in collaboration with associations, industry and other academic institutions Strategic skills Drug discovery and development Understand drug discovery and development including novel approaches and regulatory perspectives Exposure to key regulatory guidance documents (e.g., FIH, PopPK, and DDIs Preclinical experiments and clinical trial designs Understand global regulatory landscape (e.g., global regulatory agencies, typical regulatory interaction passes, submission package requirements (high level), and review/approval processes) Courses over two semesters. First semester is basic, second semester in more advanced Crowd-sourced compendium of real-world case studies taught by faculty or guest lectures with experience in pharmaceutical industry or regulatory agency Include case studies where students take the role of a drug discovery or development team MID3 Understand how MID3 can be leveraged to streamline and accelerate drug discovery and development Special patient populations, such as pediatric drug development and extrapolation approaches Course that builds upon drug development block and technical blocks. Ideally, the panel should include non-technical decision makers so that students can practice telling convincing stories to non-pharmacometrics experts Conference attendance to sharpen scientific presentation skills and establish/expand scientific network Toastmasters Negotiation and influencing skills and leading drug development teams to consensus Become aware of your presence and the impact of your presence with focus on drug development teams Learn to “speak the language” of collaborators in interdisciplinary teams Specialized faculty or guest lecturer in a multiple day workshop Virtual internship/fellowships with regulatory agencies or pharmaceutical industry Note: The individual starting point for each activity may differ between students/trainees based on their prior training and experience.
Model‐informed drug development (MIDD) is critical in all stages of the drug‐development process and almost all regulatory submissions for new agents incorporate some form of modeling and simulation. ...This review describes the MIDD approaches used in the end‐to‐end development of ertugliflozin, a sodium‐glucose cotransporter 2 inhibitor approved for the treatment of adults with type 2 diabetes mellitus. Approaches included (1) quantitative systems pharmacology modeling to predict dose–response relationships, (2) dose–response modeling and model‐based meta‐analysis for dose selection and efficacy comparisons, (3) population pharmacokinetics (PKs) modeling to characterize PKs and quantify population variability in PK parameters, (4) regression modeling to evaluate ertugliflozin dose‐proportionality and the impact of uridine 5'‐diphospho‐glucuronosyltransferase (UGT) 1A9 genotype on ertugliflozin PKs, and (5) physiologically‐based PK modeling to assess the risk of UGT‐mediated drug–drug interactions. These end‐to‐end MIDD approaches for ertugliflozin facilitated decision making, resulted in time/cost savings, and supported registration and labeling.
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