The Changing Face of Drug Development, Trends and Challenges

Abstract

Drug development remains a lengthy, costly, and high-risk endeavour, typically spanning 10–15 years and requiring approximately $2.6 billion in capitalized expenditure per approved therapy, a figure that does not account for the substantial variation across therapeutic areas. Traditional discovery methods, while historically impactful, suffer from low efficiency and high attrition rates. In response, a transformative shift is underway, fuelled by breakthroughs in artificial intelligence (AI), gene and RNA therapeutics, decentralized clinical trials, and real-world evidence. AI and machine learning are revolutionizing key early-stage processes, including target identification, virtual screening, predictive toxicology, and adaptive trial designs, which help reduce cycle times by up to 18% and propel multiple compounds into clinical evaluation. Although no AI-designed drug has reached the market yet. CRISPR, CAR‑T, and mRNA therapeutics, principally driven by pandemic-era successes, are redefining treatment possibilities for genetic and infectious diseases. Parallel technical advances in digital health have enabled decentralized clinical trials, incorporating telemedicine, wearable sensors, and real-time data capture, enhancing efficiency and patient inclusion. Likewise, the integration of real-world evidence from electronic health records and patient-reported outcomes is enhancing regulatory decision-making and post-market safety assessments. However, this progress brings significant challenges: regulatory frameworks are struggling to adapt across jurisdictions; biologics manufacturing remains expensive and complex; data privacy and cybersecurity concerns are growing; and scalability risks widening global health disparities. Additionally, the recruitment of diverse and representative patient populations continues to be a major hurdle.