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Thursday, October 3 • 2:20pm - 2:30pm
Lightning Session- Employing Supervised Learning Methods To Optimize pH Targeting In Chemical Solutions

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Employing Supervised Learning Methods To Optimize pH Targeting In Chemical Solutions
Chemical solutions are ubiquitous in research, development and production within the fields of chemistry, biology, pharmaceuticals and many others. The pH of a solution is an essential property to be able to control for a large number of experimental use cases, but that control should not come at any cost: ionic strength and other experimentally-relevant criteria also need to be controlled.

We present LabMinds’s Solution Recipe Platform (SolReP), which was developed using performance data gathered from LabMinds’s Revo (SLAS New Product Award winner, 2016). The aim of the Platform’s continual development is to optimize solution compositions in order to reliably control pH in the presence of varied solution compositions. We can extrapolate control of pH outside of training regions of parameter space, including untrained combinations of chemicals. Our product improves solution composition, bringing a robustness and accuracy that’s often lacking, and with that brings a growing preference for our solution recipes and in turn more data on which to further improve those recipes: a virtuous cycle for all.

We have targeted a core set of buffer systems with pharmaceutically relevant use cases (Acetate, Citrate, Histidine, Phosphate, Tris), across a broad range of concentrations and pHs. The uptake of these solution recipes among our customers have been great, replacing their in-house solution SOPs, where regulations allow. We target an area of pH and concentration space that is larger than simple, popular calculators are suited to and develop our platform using a growing data set that is much larger than an individual scientist can rely on, when developing solution compositions in an ad hoc manner.

Solution recipe compositions have historically been developed by making use of either simple theoretical calculators, commonly with a small region of applicability and little empirical input, or trial-and-error development, at individual points of interest. Our Platform extends the complexity and validity of the calculator approach and introduces a suite of supervised learning methodologies to ensure that continual performance data improves the power of its predictions.

The pH of a solution can be predicted by mechanistic, semi-mechanistic, empirical and model-free approaches. Many of the mechanistic and semi-mechanistic models are a century old and parameters for the few chemical species that have been published are of variable quality. Using supervised learning, we can train these models, empirical models and model-free methodologies to improve their predictive power. Characterizing the identifiability of parameters is necessary both to assess the structural suitability of the models and to aid with experimental design. Parameter identifiability for the mechanistic and semi-mechanistic models can be achieved only by training in regions of pH, ionic-strength and temperature space where the various parameters dominate the dynamics. Unidentifiable parameters do not mean that a model has no predictive power, but reinforces the importance of characterizing the sensitivity of its predictions to those parameters, which we achieved by simulating experiments sampling the posterior distribution.

 LabMinds’s SolReP provides a significant step toward a data-centric approach to laboratory work.

Speakers
avatar for Ville Lehtonen, MBA, MSci

Ville Lehtonen, MBA, MSci

CEO, LabMinds
Founder and CEO of LabMinds Ltd. MBA from Oxford University, UK and MS Comp Sci from Aalto University, Finland. 

Chairs
avatar for Mike Berke, MS

Mike Berke, MS

Director of Research and Automation Technologies, Amgen
Mike is the Director of the Research & Automation Technologies (R&AT) group at Amgen, where he has been for the last 16 years. The R&AT group focuses on developing novel devices and software tools to improve research processes.


Thursday October 3, 2019 2:20pm - 2:30pm
Theater Ballroom (5th Fl) - Courtyard Boston Downtown 275 Tremont Street, Boston, MA 02116