NOVEMBER 20239TECH OUTLOOKThe more experienced the scientists, the better the assay starting conditions and the quicker the assay optimizationsoftware wise to reduce human intervention during experiments.3. AI natural language processing agent to translate a list of assay procedures in ELN (electronic lab notebook) into robotic sequences.4. AI decision agent for the volume of calibrators and QCs to prepare for a particular batch run ­ decide on the fly how much volume of calibrators and QCs are needed, calculate/plan a preparation scheme and execute accordingly.AI problem-solving agent for intelligent adjustment of dilutions ­ e.g. when the test sample volume is low, plan a lower starting volume to achieve the same dilution factor with enough volume to carry on the extraction. When there is not enough sample volume to carry on the experiment, mark it as "not enough sample," skip that sample, plan a new liquid handling scheme and carry on. AI enhancement for bioanalytical method developmentMethod development is the most intellectually challenging part of bioanalysis. Since ligand binding assays' data readout is simpler and quicker, here let us focus on LBA PK assay development. LC-MS/MS method development has its complexities, but it shares the same general principle. Current bioanalytical method development success mostly relies on scientists' experiences. The more experienced the scientists, the better the assay starting conditions and the quicker the assay optimization. But for less experienced scientists, a more general and robust approach is to use the design of experiment, or DoE, which is essentially systematic, multivariate optimization.If all the above AI enhancements are implemented and integrated with peripheral instruments such as plate washer, plate sealer, incubator, plate reader and assorted commercial software, we would have a robot that takes human input on assay procedures and initial assay parameters, plans DoE experiments, executes the DoE experiment, interprets the data, decides on the best parameters, plans a bioanalytical run, executes the run, collects data and curve-fits data, decides whether the assay performance is acceptable or not, and if not, decides new parameters to repeat the process. This robot can work tirelessly day and night, until it reaches a satisfactory assay condition, or hits some boundary conditions set by humans, or runs out of lab consumables.ConclusionWith the accelerated advancement of AI in the past few years, non-bench based bioanalytical work could see AI enhancement first. Intelligent robots that can assist bioanalysis and life sciences bench work in general are now technologically feasible in principle. The things these robots can achieve are endless. However, current mainstream AI development effort focuses on mass AI applications such as smart search engines and self-driving cars. The business case for AI-empowered robotics for bioanalysis and life sciences as a whole is still incubating. As before, I strongly believe that it is only a matter of time before AI finally permeates into life sciences, with this kind of advanced robotics taking their place in every bioanalytical laboratory, freeing up bioanalytical scientists from much physical work and letting them focus on the sciences.
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