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.



 

Immuno-oncology

(first presented at AACR Annual Meeting 2015)

 

Customer situation:

        Jounce Therapeutics, Biotech company in Immuno-Oncology space

        Thought experiments for PD-1 – Tim3 dual targeting

        BMS and Merck anti-PD-1s therapeutic antibodies have affinities that differ by two orders of magnitude. Why are dosing regimens so similar?

        Sensitivity analysis for experiment prioritization

        Predict optimal drug properties targeting PD-1 and TIM3 in oncology for:

        Bispecific biologics (Formats: 2-2, 2-1, 1-2, 1-1)

        Fixed dose combinations (FDC)

        Perform risk assessment by performing an in silico differentiation for a bispecific vs. FDC

        Timelines: about 4 months

 

Customer question:

        Why are the dosing regimens for existing therapeutic antibodies so similar?

 

Results:

Enabled quantitative decision making that impacted high value questions, strategy, and critical thinking, years before going into the clinic:

        Provided insights as to why the dosing regimens of the two anti-PD-1s are roughly similar

        Identified list of sensitive parameters and aided in experimental design to estimate unknown sensitive parameters (TIM3 Kd and expression, and TMDD)

        Predicted optimal drug parameters for bispecific formats vs. FDC

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