A Modular Mechanistic In Silico Model for In Vitro Transcription Process Yield and Product Quality Prediction

TL;DR

This paper introduces a modular hybrid mechanistic and machine learning model for predicting mRNA yield and quality in in vitro transcription, aiding process optimization and understanding.

Contribution

It develops a scalable, hybrid modeling framework combining biochemical principles and machine learning for in silico IVT process prediction and optimization.

Findings

Identified key mechanisms affecting mRNA yield and quality.

Enabled efficient parameter estimation through Bayesian optimization.

Supported rational design of mRNA manufacturing processes.

Abstract

In vitro transcription (IVT) plays a critical role in the manufacture of mRNA vaccines and therapeutics. Optimizing mRNA yield and ensuring product quality, such as capping efficiency and integrity, are essential but mechanistically complex. This study presents a modular mechanistic model of the IVT process to advance scientific understanding and improve predictive capability. The IVT reaction network is decomposed into interconnected modules describing (1) initiation and capping, (2) elongation and truncation, (3) termination and read-through, (4) mRNA degradation, (5) magnesium pyrophosphate precipitation, and (6) enzymatic degradation of pyrophosphate. Guided by biochemical principles and experimental data, kinetic models were developed for each module, accounting for mass balances, molecular complexation, and enzyme activity, and were subsequently assembled to capture coupled IVT dynamics. Multivariate residual analysis and Shapley value-based sensitivity analysis, guided by domain knowledge, were applied to iteratively improve model fidelity. These machine learning-driven analytics enabled identification of key mechanisms, supported in silico experimentation, and facilitated root-cause analysis. Combined with Gaussian-process-based batch Bayesian optimization for efficient parameter estimation, this framework establishes a scalable hybrid (mechanistic + machine learning) modeling platform that integrates heterogeneous data, accelerates model calibration, and supports rational design and optimization of mRNA manufacturing processes.