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Reduced Animal Testing

  • AI-powered biosimulations replicate biological processes & systems using computational models.

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  • Predict a drug's pharmacokinetics, metabolism, and potential toxicity before clinical trials, allowing for early rejection of unsuitable drug candidates, minimising reliance on animal testing and promoting ethical, efficient drug development.

Fast

Real-Life Examples:

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In the approval of IV secukinumab, PBPK modelling replaced the need for a full clinical trial to determine dose equivalency with its subcutaneous form, saving time and ensuring correct drug exposure.

Safe

Real-Life Examples:

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PBPK models simulate how drugs behave in vulnerable populations (e.g. liver/kidney impairment), enabling safer, pre-emptive dose adjustments.

 

This helps to avoid toxicity-related trial failures and reduce patient harm from adverse reactions.

 

Instead of relying on trial-and-error dose escalation in Phase I studies, PBPK simulations help identify optimal dosing regimens upfront, shortening development timelines.

5 :: IMPACT ON HEALTHCARE & DRUG DEVELOPMENT

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(McNally K., et. al, 2011)

Physiologically Based Pharmacokinetic Modelling:

Faster & Safer Drug Approvals Through Improved PBPK Modelling

  • PBPK modelling is a mechanistic approach to predict drug pharmacokinetics using anatomical, physiological, biochemical, and molecular data.

    • These models simulate the ADME of drugs by mathematically representing organs and tissues as compartments with defined blood flows and enzymatic activity.

Main application areas:

From 2019 to 2023, 243 novel drugs were approved by the FDA, of which 74 novel drug applications used the PBPK models.

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(Sun Z., et.al, 2024)

In personalised medicine, PBPK models:

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  • Account for patient-specific variables

    • (e.g. age, weight, organ function and disease states.)

  • Enable virtual clinical trials by predicting drug behaviour in diverse populations

    • (e.g. paediatrics, elderly, renal impairment).

  • Support dose individualisation by integrating genetic, physiological, and pathological parameters.

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These models are instrumental in evaluating complex drug interactions, adjusting dosing in special populations, and bridging gaps between in vitro and in vivo data.​ 

This allows researchers to extrapolate laboratory findings to predict systemic exposure, organ distribution, and dosing outcomes in real-life biological systems.

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