Big Pharma’s ability to innovate has grown in recent years, and the industry’s increasing reliance on data could help it sustain momentum in the future.
While McKinsey notes that the industry has been relatively slow in adopting technologies such as AI and automation, the industry is growing more tech-savvy.
“The acceptance of data is picking up across the industry,” said David Harel, co-founder and president of CytoReason, an Israeli startup working with six of the top ten pharma companies.
At the same time, pharmaceutical research is headed in a much more collaborative direction where partnership and licensing are common, said Ed White, chief analyst and VP of IP and innovation research at Clarivate.
The pharma industry has traditionally had a large amount of data to tap in drug discovery and development, but the volume of data in the field is skyrocketing.
Data science techniques are thus vital to tame the chaos. “Data science is also becoming critical in clinical development, specifically, in Phase 2 trials where the true effect of the drug is shown,” Harel said. “A lot of capital has been invested in bringing the asset to that stage,” Harel said. “And still a lot more needs to be spent to bring it to the market and to understand who the patients would be that would benefit from it and how it would fare in the market against competition.”
Data science can also help pharma companies looking to win new indications for a drug already on the market. Biologics that neutralize inflammatory cytokines, for instance, could potentially offer benefits to patients suffering from multiple types of chronic immune-mediated inflammatory diseases.
With the quickly expanding use of data science in the industry, many Big Pharma companies are working with partners who can help them deploy data analytics, machine learning and other tools.
Harel said that the interaction between AI-focused startups and pharma companies is “valuable for both parties,” Harel said.
There are simply not enough data scientists available for pharma companies and biotechs to do such work internally.
While large pharma companies are uniquely positioned to launch Phase 3 trials and commercialize new drugs, smaller AI-specialist companies focusing on drug discovery and development are often more agile and innovative.
CytoReason, for instance, has developed a cell-centered computational model of human disease that can simulate disease in cells and tissues.
Computer models can also reduce pharma companies’ reliance on animal testing in drug development.
“There are things that smaller companies bring to the table that bigger companies often struggle to produce,” Harel said.
Filed Under: Data science