Phase-1 Interim Analysis

Once a drug candidate progresses into the clinic, quantitative systems pharmacology (QSP) modeling can be applied to help with critical issues such as observed variability in clinical data or how the candidate is fairing against competitors. The following case study is an example where QSP modeling was introduced into a project for the first time after Phase 1 for the purpose of explaining observed variability and nonlinearity which in turn saved a molecule from being discarded and helped position it as best-in-class.

Immuno-modulation in chronic inflammation


Customer situation (Healthy Volunteers):

        Empirical PK/PD modeling used to enable Phase 1 (not systems pharmacology)

        Competitor molecule still ahead in the clinic

        Dose administration and frequency still major Go/No-Go criteria


        High non-linear PK (IV and SC dosing)

        High PK variability with SC dosing

Customer’s Questions:

        Can the non-linear PK and SC PK variability be explained?

        Will it be possible to achieve >90% target inhibition in patients?

        Should program be discontinued? Refocused on another disease indication?


        Model results used to amend Ph1 protocol, to prepare Medicine and Marketing for counterintuitive Ph1 results and to obtain regulatory approval to change Ph2 trial design

        Customer’s best-in-class molecule now positioned to be first-in-class as well (competitor postponed clinical trials)

Related Case Studies

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When designing a biological therapeutic agent, it is critically important to establish the feasibility of achieving a desired target product profile (TPP) as early in the program as possible, typically at the ‘New Target Proposal Stage’ or at the start of Lead Identification (LI). This case studies outlines an example of where systems pharmacology modeling and analysis helped eliminate targets with low developability and helped set up a screening funnel for top candidates.

Clinical Candidate Selection

When selecting a clinical candidate, it is a competitive advantage to have accurate, quantitative predictions that help answer strategic questions such as whether a program should be terminated, if a new lead generation campaign is needed, or what further assays need to be developed. This case studies outlines an example where systems pharmacology modeling helped save a clinical candidate from being terminated and instead accelerated it into the clinical where it is currently slated to be best-in-class.

Predicting optimal drug parameters and doses

In early drug development, quantitative systems pharmacology (QSP) modeling can provide decision support for many critical decisions such as what experiments to proceed with and what parameters have the most impact. It can also lend insight into a drug candidate's competitive landscape. This case study outlines an example where QSP modeling helped assess why the dosing regimens of the two anti-PD-1s are roughly similar and predicted optimal drug parameters for bispecific formats versus fixed dose combination.

Antibody drug conjugate (ADC) Pharmacokinetics

With an ever increasing focus on antibody drug conjugates (ADC), computational models are needed to better understand the complexity of the various design parameters and objectives of ADCs. The following case study is a computational exploration of mechanistic determinants of ADC pharmacokinetics using quantitative systems pharmacology (QSP) modeling strategies.