IVIG co-administration with monoclonal-antibodies/bispecifics is common across autoimmunity, immunodeficiencies, and transplantation. These therapies undergo FcRn-mediated recycling, raising potential for PK-PD interactions. Mechanistic IgG-disposition model utilized to investigate how IVIG dose/timing, and IgG dynamics influence IgG behavior and PK/PD outcomes. Model well-described total/endogenous-IgG kinetics across autoimmune/transplantation. It recapitulated reduced tesidolumab exposure with IVIG in transplantation, explaining associated PD alterations. Model examined IVIG supplementation in multiple-myeloma receiving teclistamab, and rituximab-IVIG co-therapy for transplant desensitization. Simulations revealed IVIG co-therapy alters mAb PK-PD depending on IVIG-regimen, IgG-pool and exposure-response-relationship of the mAb. Model provides mechanistic framework for MIDD and prospective management of IgG interactions. The MIDD Webinar Series, coordinated by Yash Kapoor and Fulya Akpinar Singh, is a series of webinars focused on shaping the future of drug development and regulatory decision-making sponsored by the ISoP Education Committee. Topics range from MIDD approaches in regulatory submission to pharmacometrics topics that are at the core of model development.

The ICH M15 Guideline “General Principles for MIDD” stresses the importance of performing model evaluation targeting the Question of Interest (QoI) that model is intended to help to answer. In this presentation we elaborate on how reference corrected visual predictive check (rcVPC) methodology can be used for this purpose. The rcVPC can provide an efficient and more easily communicable tool to validate models, increasing confidence in model-informed decision-making among broader, non-modeling stakeholders. The MIDD Webinar Series, coordinated by Yash Kapoor and Fulya Akpinar Singh, is a series of webinars focused on shaping the future of drug development and regulatory decision-making sponsored by the ISoP Education Committee. Topics range from MIDD approaches in regulatory submission to pharmacometrics topics that are at the core of model development.

Transcription factors are important molecules which control the levels of mRNA and proteins within cells by modulating the process of transcription and hence translation. Transcription factors are part of a wider family of molecular interaction networks known as gene regulatory networks (GRNs) which play an important role in key cellular processes such as cell division and apoptosis (e.g. the p53-Mdm2, NFκB pathways). Transcription factors exert control over molecular levels through feedback mechanisms, with proteins binding to gene sites in the nucleus and either up-regulating or down-regulating production of mRNA. In many GRNs, there is a negative feedback in the network and the transcription rate is reduced. Typically, this leads to the mRNA and protein levels oscillating over time and also spatially between the nucleus and cytoplasm. When experimental data for such systems is analyzed, it is observed to be noisy and in many cases the actual numbers of molecules involved are quite low. In order to model such systems accurately and connect with the data in a quantitative way, it is therefore necessary to adopt a stochastic approach as well as take into account the spatial aspect of the problem. In this talk, we present both deterministic and stochastic spatio-temporal models of both observed GRNs and synthetic GRNs e.g. repressilators and activator-repressor systems.