A Perspective on Quantitative Systems Pharmacology Applications to Clinical Drug Development

Bai, Jane P. F.; Earp, Justin; Strauss, David G.; Zhu, Hao

doi:10.1002/psp4.12567

Cited by 10 publications

(6 citation statements)

References 10 publications

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“…Addition of model components to describe the regrowth observed may be important to elucidate the PK/PD relationship of the main, susceptible population. It may not necessarily be so that by adding more mechanistic components and expanding into a larger model, predictions are more reliable—a smaller model may be preferable 32 . Moreover, it is not likely that the host defense will differ a susceptible from a resistant bacterium and therefore the most important aspect may be to quickly reduce the bacteria population to a size manageable by the immune system.…”

Section: A Pk/pd Model Approach For Translationmentioning

confidence: 99%

Pivotal Role of Translation in Anti‐Infective Development

Friberg

2021

Clin Pharma and Therapeutics

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The value of model‐based translation in drug discovery and development is now effectively being recognized in many disease areas and among various stakeholders. Such quantitative approaches are expected to facilitate the selection on which compound to prioritize for successful development, predict the human efficacious dose based on preclinical data with adequate precision, guide design, and de‐risk later development stages. The importance of time‐dependencies, which are typically species‐dependent due to different turnover rates of biological processes, is, however, often neglected. For bacterial infections, the choice of dosing regimen is typically relying on preclinical pharmacokinetic (PK) and pharmacodynamic (PD) data, because the bacterial load and disease severity, and consequently the PK/PD relationship, cannot be quantified well on clinical data, given the low‐information end points used. It is time to recognize the limitations of using time‐collapsed approaches for translation (i.e., methods where targets are based on summary measures of exposure and response). Models describing the full time‐course captures important quantitative information of drug distribution, bacterial growth, antibiotic killing, and resistance development, and can account for species‐differences in the PK profiles driving the killing. Furthermore, with a model‐based approach for translation, we can take a holistic approach in development of a joint model for in vitro, in vivo, and clinical data, as well as incorporating information on the contribution of the immune system. Such advancements are anticipated to facilitate rational decision making during various stages of drug development and in the optimization of treatment regimens for different groups of patients.

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Section: A Pk/pd Model Approach For Translationmentioning

confidence: 99%

Pivotal Role of Translation in Anti‐Infective Development

Friberg

2021

Clin Pharma and Therapeutics

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“…Simultaneous evaluation of multiple biomarkers offered a valuable perspective for enhancing dose selection. QSP models possess extrapolative prediction capabilities, which allows for prospective selection of appropriate dosing regimens for specific patient populations, facilitating the design of clinical trials with increased precision and efficiency 40 . Recently, the application of artificial intelligence techniques in drug development is emerging.…”

Section: Discussionmentioning

confidence: 99%

“…QSP models possess extrapolative prediction capabilities, which allows for prospective selection of appropriate dosing regimens for specific patient populations, facilitating the design of clinical trials with increased precision and efficiency. 40 Recently, the application of artificial intelligence techniques in drug development is emerging. The cutting-edge artificial intelligence approaches could potentially benefit the dose individualization for siRNA products.…”

Section: Discussionmentioning

confidence: 99%

Application of Model‐Informed Drug Development in Dose Selection and Optimization for siRNA Therapies

Yuan,

Li,

Earp

et al. 2024

The Journal of Clinical Pharma

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The application of model‐informed drug development (MIDD) has revolutionized drug development and regulatory decision making, transforming the process into one that is more efficient, effective, and patient centered. A critical application of MIDD is to facilitate dose selection and optimization, which play a pivotal role in improving efficacy, safety, and tolerability profiles of a candidate drug. With the surge of interest in small interfering RNA (siRNA) drugs as a promising class of therapeutics, their applications in various disease areas have been extensively studied preclinically. However, dosing selection and optimization experience for siRNA in humans is limited. Unique challenges exist for the dose evaluation of siRNA due to the temporal discordance between pharmacokinetic and pharmacodynamic profiles, as well as limited available clinical experience and considerable interindividual variability. This review highlights the pivotal role of MIDD in facilitating dose selection and optimization for siRNA therapeutics. Based on past experiences with approved siRNA products, MIDD has demonstrated its ability to aid in dose selection for clinical trials and enabling optimal dosing for the general patient population. In addition, MIDD presents an opportunity for dose individualization based on patient characteristics, enhancing the precision and effectiveness of siRNA therapeutics. In conclusion, the integration of MIDD offers substantial advantages in navigating the complex challenges of dose selection and optimization in siRNA drug development, which in turn accelerates the development process, supports regulatory decision making, and ultimately improves the clinical outcomes of siRNA‐based therapies, fostering advancements in precision medicine across a diverse range of diseases.

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“…The awareness of QSP modeling within regulatory agencies such as the FDA has grown as the modeling approach has been increasingly utilized. Currently, the FDA has not issued any direct guidance about NASH QSP modeling, but the agency has indicated that there are certain recommendations that should be applied to all QSP modeling ( 18 , 22 ). Model transparency enables reviewers to have access to all equations and parameters used to generate simulation results.…”

Section: Manuscriptmentioning

confidence: 99%

Applications of Quantitative Systems Pharmacology (QSP) in Drug Development for NAFLD and NASH and Its Regulatory Application

Siler

2022

Pharm Res

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Nonalcoholic steatohepatitis (NASH) is a widely prevalent disease, but approved pharmaceutical treatments are not available. As such, there is great activity within the pharmaceutical industry to accelerate drug development in this area and improve the quality of life and reduce mortality for NASH patients. The use of quantitative systems pharmacology (QSP) can help make this overall process more efficient. This mechanism-based mathematical modeling approach describes both the pathophysiology of a disease and how pharmacological interventions can modify pathophysiologic mechanisms. Multiple capabilities are provided by QSP modeling, including the use of model predictions to optimize clinical studies. The use of this approach has grown over the last 20 years, motivating discussions between modelers and regulators to agree upon methodologic standards. These include model transparency, documentation, and inclusion of clinical pharmacodynamic biomarkers. Several QSP models have been developed that describe NASH pathophysiology to varying extents. One specific application of NAFLDsym, a QSP model of NASH, is described in this manuscript. Simulations were performed to help understand if patient behaviors could help explain the relatively high rate of fibrosis stage reductions in placebo cohorts. Simulated food intake and body weight fluctuated periodically over time. The relatively slow turnover of liver collagen allowed persistent reductions in predicted fibrosis stage despite return to baseline for liver fat, plasma ALT, and the NAFLD activity score. Mechanistic insights such as this that have been derived from QSP models can help expedite the development of safe and effective treatments for NASH patients.

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A Perspective on Quantitative Systems Pharmacology Applications to Clinical Drug Development

Cited by 10 publications

References 10 publications

Pivotal Role of Translation in Anti‐Infective Development

Pivotal Role of Translation in Anti‐Infective Development

Application of Model‐Informed Drug Development in Dose Selection and Optimization for siRNA Therapies

Applications of Quantitative Systems Pharmacology (QSP) in Drug Development for NAFLD and NASH and Its Regulatory Application

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