Process development at early clinical stages
During the early clinical development phase, Jesse McCool, cofounder and CEO of Wheeler Bio believes the best place to invest in digitalization is in the process development laboratory where representative (and more affordable) scaled-down models can be leveraged to generate data points in an efficient manner. “Digitalization can aid process scientists in the ML cycle where we design, test, build, learn, and repeat, in an iterative fashion, to create powerful, predictive models of our biomanufacturing processes. The data wrangling is perhaps the hardest part when implementing ML in the lab and digitalization solutions are key [to] reducing the manual steps in data processing,” he explains. The process is iterative in nature, so according to McCool, digitalization tools are most useful at this stage to keep costs down and expedite insight building.
Drug development can also benefit from various aspects of digitalization, such as lead definition through the use of AI, according to Vincenza Pironti, senior pharma services R&D staff scientist at Thermo Fisher Scientific. She notes that computational chemistry can be used as well to broaden the pool of potential candidates and explore non-standard landscapes, thus expanding pipelines. “In summary, three values are widely acknowledged as the major benefits of digitalization at early development phases: high productivity, possible timing reduction in drug discovery, and diversified pipelines,” Pironti concludes.