Chilakwad, Shama; (2022) Automation and analysis of high-dimensionality experiments in biocatalytic reaction screening. Doctoral thesis (Eng.D), UCL (University College London).

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Abstract

Biological catalysts are increasingly used in industry in high-throughput screening for drug discovery or for the biocatalytic synthesis of active pharmaceutical intermediates (APIs). Their activity is dependent on high-dimensionality physiochemical processes which are affected by numerous potentially interacting factors such as temperature, pH, substrates, solvents, salinity, and so on. To generate accurate models that map the performance of such systems, it is critical to developing effective experimental and analytical frameworks. However, investigating numerous factors of interest can become unfeasible for conventional manual experimentation which can be time-consuming and prone to human error. In this thesis, an effective framework for the execution and analysis of highdimensionality experiments that implement a Design of Experiments (DoE) methodology was created. DoE applies a statistical framework to the simultaneous investigation of multiple factors of interest. To convert the DoE design into a physically executable experiment, the Synthace Life Sciences R&D cloud platform was used where experimental conditions were translated into liquid handling instructions and executed on multiple automated devices. The framework was exemplified by quantifying the activity of an industrially relevant biocatalyst, the CV2025 ωtransaminase enzyme from Chromobacterium violaceum, for the conversion of Smethylbenzylamine (MBA) and pyruvate into acetophenone and sodium alanine. The automation and analysis of high-dimensionality experiments for screening of the CV2025 TAm biocatalytic reaction were carried out in three sequential stages. In the first stage, the basic process of Synthace-driven automated DoE execution was demonstrated by executing traditional DoE studies. This comprised of a screening study that investigated the impact of nine factors of interest, after which an optimisation study was conducted by taking forward five factors of interest using two automated devices to optimise assay conditions further. In total, 480 experimental conditions were executed and analysed to generate mathematical models that identified an optimum. Robust assay conditions were identified which increased enzyme activity >3-fold over the starting conditions. In the second stage, nonbiological considerations that impact absorbance-based assay performance were systematically investigated. These considerations were critical to ensuring reliable and precise data generation from future high-dimensionality experiments and include confirming spectrophotometer settings, selecting microplate type and reaction volume, testing device precision, and managing evaporation as a function of time. The final stage of the work involved development of a framework for the implementation of a modern type of DoE design called a space-filling design (SFD). SFDs sample factors of interest at numerous settings and can provide a fine-grained characterisation of high-dimensional systems in a single experimental run. However, they are rarely used in biological research due to a large number of experiments required and their demanding, highly variable pipetting requirements. The established framework enabled the execution and analysis of an automated end-toend SFD where 3,456 experimental conditions were prepared to investigate a 12- dimensional space characterising CV2025 TAm activity. Factors of interest included temperature, pH, buffering agent types, enzyme stability, co-factor, substrate, salt, and solvent concentrations. MATLAB scripts were developed to calculate important biocatalysis metrics of product yield and initial rate which were then used to build mathematical models that were physically validated to confirm successful model prediction. The implementation of the framework provided greater insight into numerous factors influencing CV2025 TAm activity in more dimensions than what was previously reported in the literature and to our knowledge is the first large-scale study that employs a SFD for assay characterisation. The developed framework is generic in nature and represents a powerful tool for rapid one-step characterisation of high-dimensionality systems. Industrial implementation of the framework could help reduce the time and costs involved in the development of high throughput screens and biocatalytic reaction optimisation.

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