At the forefront of modeling & simulation

A major challenge in the drug development process is drug attrition in Phase 2 clinical trials.  In fact, approximately 80% of new drugs that move into Phase 2 fail, wasting significant investment and time.  The major reason for failure is that the drug doesn’t show efficacy or is not safe.

Using Simcyp™ Quantitative Systems Pharmacology (QSP) to augment modeling and simulation helps tackle this issue.

  • QSP integrates quantitative drug data with knowledge of its mechanism of action
  • QSP models predict how drugs modify cellular networks in space and time and how they impact and are impacted by human pathophysiology
  • QSP facilitates evaluating complex, heterogeneous diseases such as cancer, immunological, and metabolic diseases that may require combination therapies

Per US FDA’s Issam Zineh,“QSP is arguably on an expedited pathway because of the very thoughtful work being conducted by drug development, regulatory and academic scientists. The impact of QSP is to de-risk a drug development program as it progresses.”

Our leading QSP scientific consultants partner closely with you to develop a QSP model and address key questions to de-risk your discovery and development program.

Bridging scientific gaps
  • Advance precision medicine: Plan which subpopulation to target before running that make-or-break Phase 2 trial. 
  • Increase likelihood of demonstrating drug efficacy:  Once we know how much drug is at the site of action, what pharmacological action will it have? 
  • Provide insight into mechanisms of toxicity: QSP determines the exposure at various organs to predict potential side effects. 
  • Perform “what if” scenarios:  Determine the likely efficacy of the drug, without having to do clinical investigation.
  • Support discovery of new drugs:  Using QSP models helps integrate the data being generated from the “omics sciences”

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Advancing combination immuno-oncology therapy

Traditionally, pre-clinical studies are used to test potential IO combinations. Promising data from these preclinical studies can then be moved into clinical development.

Unlike other therapeutic areas where preclinical models can be used fairly confidently to guide development, oncology models with immunological complexities have limited translational value. A QSP approach may better predict effective drug combinations, especially to more accurately correlate the physiological differences between preclinical models and human patients. 

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Tackling immunogenicity with QSP

Although success of biologics has been demonstrated, there are inherent operational and technological challenges associated with this complex class of drugs. One of these challenges—immunogenicity (IG)—has become a key area of regulatory interaction.

Immunogenicity is defined by the FDA as the propensity of the therapeutic protein to generate immune responses to itself and to related proteins, or to induce immunologically-related adverse clinical events

A QSP-based approach can help to predict and
better manage immunogenicity and guide clinical and regulatory decision-making in biologics drug development.

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Piet van der Graaf, PharmD, PhD Senior Vice President, Quantitative Systems Pharmacology

With over 20 years of experience working in the pharmaceutical industry at Sanofi and Pfizer, Piet brings considerable skill and experience to QSP projects and contributes to the strategic development of Certara.  He is also Editor-in-Chief of CPT: Pharmacometrics & Systems Pharmacology.

Cesar Pichardo, PhD Head of QSP Consultancy

Cesar has more than 20 years of experience developing biological, physiological, and medical models for lifestyle interventions, drug development, mortality risk, and actuarial science. He obtained a Chemical Engineering (MEng) and a MSc in Systems Engineering (Control Theory) from Simon Bolivar University (Venezuela), followed by completing a PhD in Applied Mathematics from Ecole Centrale de Lille (France).