Mathematical Models can Assist in Broadening use of Immuno-oncology Drugs to Treat Autoimmunity

Bispecific antibodies and T cell engagers (TCE) are exciting new cancer treatments because of their potency, target specificity, and improved developability vs cellular therapies. For these same reasons, they could be the next frontier in autoimmune therapies. A mechanistic QSP model can predict the efficacious doses of repurposed bispecifics by integrating the known binding properties, mechanism of action, pharmacokinetics (PK) and pharmacodynamic (PD) responses with quantitative details of the drug targets and desired metrics of efficacy.

There are significant safety risks involved with any immunotherapy that must be carefully evaluated to achieve the optimal therapeutic index. In non-cancer therapies like autoimmunity, the bar is even higher for mitigating toxic side effects. Both CAR-T cells and TCEs can induce Cytokine Release Syndrome (CRS) in cancer patients, although the severity of symptoms vary widely. Given the already hyperactivated state of the immune system in autoimmune disorders, any further perturbation, such as increased cytokines associated with TCEs and CAR-T cells, could be cause for alarm. This means that both starting doses and efficacious doses may have to be adjusted significantly in clinical trials to avoid life-threatening risks during repurposing. Instead of an empirical approach of using safe doses from other indications, mechanistic modeling of drug-induced toxicology can provide more insight into potential risk factors and assist dose selection, frequency and route of administration. With the extreme clinical heterogeneity in any given autoimmune disorders as well as the variability in immunotherapy-induced toxicity incidences, there may be a subset of patients that benefit from these drugs while others are at particularly high risk; simulation of virtual patients by QSP modeling can help with streamlining of clinical trials (Chelliah et al., 2021). In addition, feasibility of mitigation strategies (such as priming doses, cytokine blockade etc) and their timings can also be tested in silico to alleviate some of these concerns. In the end, the choice of a repurposed drug may come down to a balance of safety and efficacy. In IO applications, TCEs generally trigger CRS less than CAR-T cells, but are slightly less efficacious than CAR-T cells. Quantitative models may help companies evaluate these same risks and tradeoffs in the autoimmune space.

Repurposing of drugs for new indications is a long-standing practice in the pharmaceutical industry. While some notable examples of repurposing happened by chance in the past, the likelihood of successful applications have increased with the combination of advanced omics technologies that delve into disease signatures and computational methods (data mining, machine learning, artificial intelligence) that integrate drug-target-host interactions (Pawar et al., 2023). The same will be true for mechanistically informed QSP models that have shown to be effective in guiding dose projection, drug and study design. Through collaboration and thoughtful partnership, these quantitative approaches can leverage the investment made in the IO space to bring a new generation of therapies to those suffering from autoimmune diseases with the appropriate urgency.

Special thanks to Selena Lorrey for comments on this piece.