MS05 - OTHE-2
Interfaith Prayer and Reflection Room (#3020C) in The Ohio Union

Preparing for the Next Pandemic: Modeling and Simulation in Drug Development

Wednesday, July 19 at 10:30am

SMB2023 SMB2023 Follow Wednesday during the "MS05" time block.
Room assignment: Interfaith Prayer and Reflection Room (#3020C) in The Ohio Union.
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Celeste Vallejo


Preparing for the next pandemic is going to require cooperation and coordination amongst many groups of people. One such group is those working in drug development. Drug development is a crucial piece of the puzzle in preparing for the next pandemic. This is exemplified in the effect that the COVID-19 vaccine had on the outcome of this most recent pandemic. In this mini-symposium, speakers from across the drug development space are coming together to describe various ways in which modeling and simulation in drug development can be used to prepare for the next pandemic. The goal of this session is to inform the mathematical biology community, especially those that have or continue to work with government officials, on how those in the drug development space are thinking about problems related to drug development, what tools are being used, and the contributions that can be made towards preparing for the next pandemic.

Sriram Chandrasekaran

University of Michigan (Biomedical Engineering)
"Drug discovery and repurposing using hybrid machine learning and biochemical modeling"
By 2050, we may lose 10 million people a year to drug-resistant infections. Unfortunately, the pace of drug discovery has not kept up with the rapid emergence of these pathogens. Drug combinations have great potential to reduce the spread of drug-resistant bacteria. However, current drug-discovery approaches are unable to screen an astronomical number of drug combinations and do not account for pathogen heterogeneity or the complex in vivo environment. We have developed hybrid AI tools - INDIGO, MAGENTA, and CARAMeL, which predict the efficacy of drug regimens based on the properties of the drugs, the pathogen, and the immune and infection environment. Our hybrid AI methods combine engineering models with machine learning, which provides both predictive power and mechanistic insights. Using these methods, we have identified highly synergistic drugs to treat drug resistant infections including Tuberculosis, the world's deadliest bacterial infection. Our approach also accurately predicts the outcome of past clinical trials of drug regimens. Our ultimate goal is to create a personalized approach to treat infections using AI.

Amber Smith

University of Tennessee Health Science Center (Department of Pediatrics)
"PKPD modeling of Plasmodium falciparum ATP4 inhibitor SJ733 with the pharmacokinetic enhancer cobicistat"
SJ733 is a newly developed inhibitor of Plasmodium falciparum ATP4 with a favorable safety profile and rapid antiparasitic effect but insufficient duration to deliver a single-dose cure of malaria. To better understand the dynamics and predict cure regimens, we developed a PKPD model. The PK could be captured using a two-compartment model with enterohepatic recirculation. Pairing this with a mechanistic PD model suggested that efficacy was increased post-recirculation and that increasing exposure would be required for cure. This prompted us to measure PK profiles for multidose regimens with or without a pharmacoboost approach using cobicistat. Either approach could significantly increase exposure but with varying kinetics. Refitting the PK model and pairing it with the PD model predicted that an unboosted, multidose regimen would increase parasite clearance by ~3x compared to 5x in the cobicistat-boosted group. The simulations also showed that a reduction in parasite burden of 1e9 would require a minimum of 300 mg SJ733+cobicistat for 2 d or 600 mg SJ733 for 3 d or 200 mg for 4 d. These results provided candidate dosing approaches to move forward into Phase 2 trials against acute, uncomplicated malaria.

Celeste Vallejo

Simulations Plus, Inc. (DILIsym Services)
"Potential application of a mechanistic model of chronic lung disease to the treatment of post-COVID lung fibrosis and other respiratory pandemics"
Idiopathic pulmonary fibrosis (IPF) is a chronic condition in which the lungs become filled with scar tissue, reducing the amount of healthy lung tissue, thus making it difficult to breathe. There is no known cure for IPF, however some treatments have been shown to slow disease progression. IPFsym is a quantitative systems pharmacology (QSP) model for IPF developed to support drug development efforts. It mechanistically represents human pathophysiology including inflammation (e.g., neutrophils, macrophages, cytokines) and fibrosis (e.g., fibroblasts, extracellular matrix) based on human data. The integrated pathophysiology is linked to clinical outcomes like forced vital capacity (FVC). IPFsym includes simulated patients with disease progression comparable to real patients, and responses to approved treatments, pirfenidone and nintedanib, that align with clinical data. IPFsym has been applied to support clinical trial design for drugs in development. Because IPFsym includes many elements common to the fibrotic sequelae of infectious respiratory disease, there is tremendous opportunity to pivot towards pulmonary fibrotic diseases caused by respiratory pandemics (such as COVID-19). The process of model modifications and re-optimization involved in pivoting to a new indication (i.e., post-COVID-19 lung fibrosis) is illustrated through the successful conversion of IPFsym to a model of interstitial lung disease associated with systemic sclerosis (SSc-ILD).

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Annual Meeting for the Society for Mathematical Biology, 2023.