Development of an R Shiny application to aid pre-clinical development of immune-oncology compounds at Genmab
Asina Gijasi (1,2), Parth Upadhyay (1), Sieto Bosgra (1)
(1) Genmab BV, the Netherlands (2) Leiden University, the Netherlands
Objectives: Monoclonal antibodies (mAbs) have been modified to promote binding affinity, half-life in circulation and effectiveness towards targets, especially in the treatment of cancer[1]. However, variability in linear pharmacokinetic (PK) properties that may exist at drug class, species or antibody platform level are often overlooked in the pre-clinical development process when investigating at an individual compound level. We at Genmab, have developed an R Shiny application to provide a visual comparison of the PK properties of a new compound to existing products to aid drug development.
Methods: An R shiny application was developed to visualize data from pre-clinical programs for comparison with new studies and products to aid with pre-clinical drug development. To this end, concentration data [AG1] from single and multiple dose studies in wildtype FcRn mice (SCID/nude) and cynomolgus monkeys was collected. Data was limited to intravenous (IV) dosing, and to linear PK to avoid influence of loss of exposure from target mediated drug disposition (TMDD) or anti-drug antibodies (ADA). Antibody drug disposition was characterized using non-linear mixed effects (NLME) modelling using NONMEM (v 7.4, ICON Development Solutions, Maryland). Antibody disposition was described by a 2-compartment model with first-order elimination from the central compartment. PK parameters were estimated using first-order conditional estimation with interaction (FOCE-I) separately for each species with inter-individual variability (IIV) [AG2] characterised on clearance (CL) and central volume of distribution (Vc). Model goodness-of-fit (GOF) was determined using differences in objective function value (OFV), visual predictive checks, goodness of fit plots, and plausibility of parameter estimates. A covariate analysis was performed to assess the influence of antibody physiochemical parameters as well as physiological differences in the animals on antibody PK. Parameter estimates from the final covariate model were used in an RxODE based simulation tool incorporated into the R shiny application.
Results: The R Shiny application allows for direct comparison of PK data from new pre-clinical programs to be overlaid on existing datasets of similar class, species or platform level. Simulations from the final covariate model in the R shiny application also allow for a visual comparison of the observed PK properties of a new compound to current predictions of a typical drug of the same class or platform in the same species. In addition, the R Shiny application also has the option to generate reports that capture all the inputs and outputs.
Conclusions: An R Shiny application was developed to visualize differences in linear PK properties and provide a platform for comparison to new antibodies in the Genmab pipeline. The application provides an insight into the pharmacological properties of current compounds and will be extended to incorporate future pre-clinical data to improve the drug development process.
References:
[1] J.-M. Dai, X.-Q. Zhang, J.-Y. Dai, X.-M. Yang, and Z.-N. Chen, “Modified Therapeutic Antibodies: Improving Efficacy,” Engineering, vol. 7, no. 11, pp. 1529–1540, Nov. 2021, doi: 10.1016/j.eng.2020.06.030.