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Modelling and Simulation

Mathematical Modelling and Simulation, or Model Informed Drug Development (MIDD), is a powerful tool to support drug development, regulatory review and investment decisions. Modelling techniques, such as PK/PD, QSP and PopPK can be applied across the drug development continuum, integrating information from diverse data sources to gain deeper insights into drug mechanism of action, predict research outcomes and help inform research decision-making.

The benefits of integrating MIDD into your drug development workflow include:

Improve decision-making and research design

MIDD provides robust, quantitative evidence, that enhances credibility and confidence in the chosen path. For example, providing evidence to support dosing and scheduling in FIH trials, shifting from Maximal Tolerated Dose to the Biological Effective Dose.

Cost and time savings

Through the prediction of research outcomes, the most promising approaches can be prioritised, enabling fewer more targeting experiments, and smaller, more efficient trial designs. MIDD helps avoid redundant research by using existing data and models to inform new research.

Regulatory support

Regulators are increasingly seeing the value of MIDD in providing supporting evidence for efficacy, providing rationale to trail design – including optimised drug dosing, and prediction of drug safety. Agencies such as the FDA and EMA now actively support and encourage the use of such techniques in submissions.

Click examples below to find out more:

Candidate Design/Selection

Leveraging QSP modelling allows you to investigate the potential impact of different drug formats/properties on therapeutic outcome, to define optimal drug parameters for your specific therapeutic modality and MOA. For example, explore impact of drug properties such as binding affinity, potency or valency for design of mAb constructs and linker stability, payload release rate, DAR when designing ADCs.

Drug Mechanism of Action

Mechanistic modelling is a useful tool to hypothesis/demonstrate a drugs MOA and how it exerts its biological effect in a virtual environment. By testing your MOA hypothesis against integrated data sets or simulating interaction with biological targets, pathways, and systems you can identify gaps in knowledge/data, provide mechanistic validation to claims of efficacy/toxicity and benchmark against competitors.

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Translational Modelling

Modelling can account for known differences in in vitro and in vivo systems, enabling you to extrapolate preclinical data to predict human outcomes. Mechanistic PK/PD models allow you to consider both the pharmacology of the drug and the biological system it acts in and can inform FIH dose selection, identification of clinically relevant biomarkers and patient populations to increase the chance of clinical success.

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Exposure/Response

As a fundamental component to MIDD, exposure/response modelling allows you to relate drug dose to PK (concentration) and relevant response markers (PD) such as efficacy, toxicity, target engagement and biomarker levels. Thorough understanding of these relationships enables you to optimise dosage (dose and schedule) for preclinical and clinical studies that strikes the right balance between benefit and risk.

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Population PK & NCA

Population modelling allows you to understand how exposure/response differs due to patient covariates (demographics, genetics, comedications etc). This can help you predict how your drug will behave in diverse patient groups and inform adjustments to dosage for specific populations if required. Non-Compartmental Analysis (NCA) allows rapid characterisation and description of your drugs PK properties.

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Clinical Trial Simulation

Mathematical models that incorporate the drugs MOA and captures available and relevant pre-clinical and clinical data can be used to simulate outcomes for doses, schedules and combinations not tested yet clinically, allowing you to optimize dosing regimens for the next stage of clinical development. Furthermore, you can account for differences in disease settings or patient populations to tailor dosage to stratified populations.

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Physiomics brings decades of experience providing mathematical modelling and simulation solutions. We therefore understand the importance of working collaboratively with our clients to develop models that best meet their needs. Some examples of our clients and partners can be found here.

 

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