A clinical candidate must meet several criteria, such as dosing administration and frequency, in order to be selected as a lead candidate.    It's critical to have a thorough understanding of the candidate's mechanism of action, assay results, and how it will fare in the market before deciding to proceed.

In this case study, we look at an example of targeting a membrane bound protein, which is often found to be involved in the pathogenesis of Immune thrombocytopenic purpura and Rheumatoid Arthritis, using a monoclonal antibody.    Our customer’s candidate appears equipotent in a whole blood assay but significantly less potent than a competitor molecule in a target cell proliferation assay.  The competitor molecule was 2 years ahead and a tighter binder.   Our goal was to provide quantitative decision-making guidance using a systems pharmacology model to help the team answer questions such as if the project should be terminated, especially given the competitor's head start, or if a new lead generation campaign should be started to find a tighter binder.

  1. Our model analysis demonstrated that a weaker binder is a better molecule


  2. Customer molecule was not discarded, rather accelerated, and now positioned to be best-in-class

The Model

The systems pharmacology model was based on first principles as a system of elementary mass-action, mechanistic PK/PD, ordinary differential equations.  The model parameters and reactions include compartment volumes, ligand concentration and turnover rates, cell numbers and turnover rates, drug administration, target-mediated drug disposition on two cell types, and endogenous drug elimination.

Systems Pharmacology model

Dose vs affinity surface plot


  1. We determined that affinity was the major parameter driving dosing advantage, with optimal affinity about 0.1 – 1 nM.
  2. The total target burden on target cells and off target cells with different turnover rates drove ‘apparent’ data discrepancy.
  3. Our model analysis demonstrated that a weaker binder is a better molecule which resulted in acceleration of clinical development, no additional assay development, and no additional lead generation.
  4. This decreased the project R&D by 6 months to 1 year and saved a potential best-in-class drug