AI will revolutionize the healthcare industry, reinventing procedures in drug discovery, development, and (pre)clinical studies says a CPHI report released this week.
Artificial intelligence (AI) holds solutions to many challenges faced by the biopharmaceutical industry including efficient cost cutting and sustainable trials along with optimized outcomes, says Bikash Chatterjee, president of Pharmatech Associates.
Over the next five years, AI will become a significant component of our industry “applied across the value chain, from drug discovery to logistics and finance, to become the basis for continued business performance and a catalyst that reduces the cost and time of bringing effective drug therapies to the patients who need them,” he argues in this year’s CPHI annual report.
Specifically, Chatterjee highlights the advantages of AI in drug development as “the potential to select and design molecular structures, to support surrogates to animal testing, to optimize drug formulation and manufacturing, and thereby drive business performance across the pharma value chain.”
His comments resonate with the recent acknowledgement by the US Food and Drug Administration (FDA) that AI and machine learning (ML) are being used more frequently in a variety of therapeutic domains and across the drug development life cycle.
Chatterjee also describes AI models as robust, accurate, and overall effective, though to achieve this practitioners must learn “how to address the model credibility requirements that will be essential to address regulatory concerns and will provide a more uniform understanding of what is required to have confidence in the model and its outputs.”
Additionally, he highlights AI as being used to address such concerns as stability issues, dissolution, and porosity in product development with the help of quantitative structure property relationship (QSPR) analysis.
“Combining AI with established characterization techniques such as computational flow dynamics, discrete element analysis and finite element analysis has the potential to rapidly characterize and optimize the formulation and product development exercise,” Chatterjee said, adding: “Considerations such as the route of administration, dosage form, and even the primary container design can be added as constraints within the analysis framework to optimize the physicochemical, therapeutic and compliance considerations.”
Moreover, trials will no longer require animal testing for establishing toxicological safety if AI takes over, Chatterjee suggests. “Replacing animal testing requires models which can demonstrate first that they are at least as good as animal testing especially for predicting the ADME [Absorption, Distribution, Metabolism and Excretion] behavior in the body.”
This offers an unprecedented opportunity to move away from animal testing by inferring toxicity mechanisms based on individual gene activities and developing safety biomarkers based on gene expression profiles.
Additionally, Chatterjee says AI has revolutionized Quality Control (QC) by improving the speed and accuracy of inspections.
AI is an inherent element of advanced manufacturing to improve designs and scale up with less development time and process waste. With significant control over quality, AI can help eradicate human error, he argues.
CPHI took place this week in Barcelona, Spain. The full report can be downloaded here.
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