AI-Powered App Addresses Process Development Capacity Restraints

Dec. 19, 2022 claims its new application delivers optimal product conditions in fewer runs, reducing raw materials costs and length of process development campaigns., provider of an enterprise-scale GxP and CFR 21 part 11 compliant platform, releases its PD Optimizer, a zero-code AI-powered application for optimizing process development (PD) and automating design of experiments (DoE). says the introduction directly addresses the needs of life sciences manufacturers, who reportedly cite PD capacity as a bottleneck to time to market and accelerating commercialization of drug pipelines.

The PD Optimizer uses a small-data AI algorithm that integrates process SME understanding and knowledge to explore the design and operational space while continuously recommending experimental settings. 

Explainability is built into the application to provide the "why" behind the recommendations. The parameters that were used along with their relative weightings are provided along with two-dimensional and multi-dimensional interactive plots to explain how operating ranges of parameters will impact the outcome of the experiments. Control of experiments and processes is often imperfect, but's models and UI reportedly show the impact those imperfections or variances have on the target outcome. This enables running predictive “what if” scenarios on how varying inputs affect experimental outcomes, according to the company.

“While the value and impact of AI in product development and manufacturing is well-accepted, most existing techniques rely on big-data based algorithms or mechanistic models. Big data is scarce in life sciences and accurate mechanistic models are often impossible to develop with speed and accuracy. Our team has overcome this challenge by using small-data algorithms focused on outcome optimization rather than predictions,” says founder and CEO, Rajiv Anand, in a press release from the company. “Digitalization of PD also addresses another critical need in life sciences manufacturing – tech transfer. When PD is done with a well-accepted digital method, knowledge transfer happens algorithmically, providing certainty and consistency in distributed manufacturing for both internal and contract (CMO).

Read the press release at:

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