Pharmacology Modelling to Accelerate Drug Development

Pharmacology Modelling to Accelerate Drug Development

Quantitative systems pharmacology (QSP) combines computational modelling and experimental methods to explore the relationship between a drug, human biology, and the disease process. QSP modelling leverages large quantities of biological and pharmacological data to increase scientific knowledge of disease pathophysiology and facilitate the investigation of different therapeutic approaches in virtual patients in virtual clinical trials. As virtual patients are used instead of real people, QSP modelling saves time and money and allows more drug doses and drug combinations to be investigated than could be practically and ethically evaluated in real life.

QSP modelling can help to identify a new drug target or biomarker, select the optimal drug dose for a vulnerable patient population or an individual patient, repurpose an existing on-market drug for a different indication or develop a new combination therapy.

QSP is a relatively new discipline, but it is already having a profound impact on decision-making at many stages along the drug development continuum. In a CPT: Pharmacometrics & Systems Pharmacology paper published in November 2022, U.S.  Food and Drug Administration (FDA) staff reported that there has been a notable increase in the number of regulatory submissions that contain QSP, including Investigational New Drug Applications (INDs), New Drug Applications (NDAs), and Biologics License Applications (BLAs) during the past several years. Their report concluded that QSP is increasingly applied to model and simulate both drug effectiveness and safety throughout the drug development process across disease areas.

The following case studies illustrate what all the excitement is about and demonstrate QSP’s potential to advance drug development.

Eisai and Biogen Inc. announced positive results from their large, global Phase 3 confirmatory Clarity AD clinical study of lecanemab on November 29, 2022. Lecanemab is an investigational antiamyloid beta (Aβ) protofibril antibody for the treatment of mild cognitive impairment due to early Alzheimer's disease (AD) with confirmed presence of amyloid pathology in the brain.

Clinical results were presented at the 2022 Clinical Trials on Alzheimer's Disease conference in San Francisco, California. Lecanemab treatment showed 31% lower risk of converting to the next stage of disease by global CDR assessment (hazard ratio: 0.69). A slope analysis using CDR-SB based on observed data and extrapolation to 30 months showed that lecanemab takes 25.5 months to reach the same level as placebo at 18 months, indicating a 7.5-month slowing of progression.

The Certara team had started to work with Eisai about two years before these results were announced and developed a QSP model to predict and help interpret the outcomes of the trial. In fact, Certara’s QSP model predicted the positive outcome of this trial about one year early, as presented at the AD PD Annual Meeting in March 2022.

AD pathogenesis is widely believed to be driven by the production and deposition of the Aβ peptide. Amyloid aggregates are classified as monomers, oligomers, protofibrils and fibrils based on their size and solubility. As antiamyloid monoclonal antibodies have different affinities for these species, it could lead to different levels of standardized uptake value ratio (SUVr) reduction and potentially different clinical outcomes.

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