AI platforms aim to solve pharmaceutical labor and productivity gaps

Modern biotechnology possesses the tools to edit genes and design therapies, yet thousands of rare diseases remain untreated. According to industry executives, the primary bottleneck has been a shortage of labor and talent to advance the work. To address these gaps, biotech companies Insilico Medicine and GenEditBio are deploying artificial intelligence. AI is becoming a force multiplier that allows scientists to address problems the industry has previously left untouched, helping automate drug discovery and optimize gene delivery.

Speaking this week at Web Summit Qatar, Alex Aliper, the president of Insilico Medicine, explained that the company aims to develop “pharmaceutical superintelligence.” Insilico’s platform analyzes biological, chemical, and clinical data to automate drug discovery steps, such as identifying whether existing drugs can be repurposed to treat ALS, a rare neurological disorder. Aliper noted the industry’s talent constraints, stating, “We really need this technology to increase the productivity of our pharmaceutical industry and tackle the shortage of labor and talent in that space, because there are still thousands of diseases without a cure, without any treatment options, and there are thousands of rare disorders which are neglected.”

While Insilico focuses on drug discovery, GenEditBio uses AI to optimize delivery mechanisms for CRISPR (gene editing technology) inside the body, known as in vivo editing. The company recently received approval from the FDA (the US regulator) to begin trials of a CRISPR therapy for corneal dystrophy. GenEditBio uses its AI platform to analyze nonviral polymer nanoparticles to predict which chemical structures can safely transport gene-editing tools to specific tissues. GenEditBio has developed an engineered protein delivery vehicle (ePDV), which functions as a virus-like particle. Tian Zhu, the co-founder and CEO of GenEditBio, noted that the approach acts like an off-the-shelf drug that works for multiple patients, making therapies more affordable and accessible to patients globally.

However, AI-driven drug discovery faces significant data limitations. Aliper pointed out that the current corpus of biological data is heavily biased over the Western world, where it is generated. Additionally, the industry faces a regulatory bottleneck. According to Aliper, there is a plateau of around 50 drugs approved by the FDA annually. To overcome these data and productivity constraints, companies are working to generate unbiased data and eventually build digital twins of humans to run virtual clinical trials. Aliper expressed hope that in 10 to 20 years, these advancements will expand therapeutic options for the personalized treatment of patients.

Why it matters

AI is being deployed to address the labor shortage and productivity bottlenecks in drug discovery and gene editing, potentially accelerating the treatment of rare diseases.