Optimizing yield in pharmaceutical fermentation presents unique challenges for data-driven modeling. Batch cycles span several days, with process parameters changing continuously in real time, while API yield is only measured at the end of the batch. This delayed feedback prevents in-process correction and limits traditional real-time control methods. Additionally, complex interactions among variables and limited historical batch data create a high-noise, data-scarce environment.
This paper presents a structured machine learning approach to investigate fermentation yield decline and identify strategies for yield recovery and optimization in a pharmaceutical bioprocess. To address delayed outcomes, the fermentation process is divided into discrete temporal stages, enabling stage-wise analysis of process behavior. This approach links parameter performance at each stage to final yield outcomes, creating surrogate indicators of batch health.
Historical high-yield batches are benchmarked against underperforming batches using descriptive statistics, distribution analysis, and process capability studies to identify critical deviations. Key parameters – including feed rates, fatty acid concentrations, pH, dissolved oxygen, dosage variables, and raw material consistency – are analyzed individually and in combination. Quartile thresholds and inhibitory limits are established to define actionable control ranges.
Three machine learning models – linear regression, CART, and random forest – are applied in an ensemble framework. Together, they capture linear trends, interpretable decision rules, and nonlinear interactions to identify optimal operating ranges, replacing trial-and-error experimentation with data-driven process optimization.
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