Big pharmaceutical companies face an innovation crisis marked by declining research and development returns, rising costs, and scientific bottlenecks, despite advances in technologies like artificial intelligence and gene editing. Regulatory pressures, drug pricing challenges, and access inequalities further complicate progress, prompting calls for new business models and ethical, equitable innovation strategies.
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The Shifting Landscape of Big Pharma
Research and development (R&D) within the pharmaceutical industry is a highly complex and expensive process with a low success rate. However, both R&D and innovation are critical for pharmaceutical companies to remain relevant by developing more efficient and effective therapeutics.
The current business model of leading pharmaceutical companies involves intensive drug discovery and acquiring external assets, such as those developed by smaller biotechnology companies. By collaborating with academic researchers, public research institutions, and other pharmaceutical companies, the pharmaceutical industry fosters a competitive and continuously evolving environment1.
Leading pharmaceutical companies have experienced significant transformations in their R&D strategies. There has been a marked shift toward balancing in-house innovation with licensing and acquisitions, partly driven by declining financial returns on internally developed assets. This shift has been characterized by reduced investments in non-clinical/preclinical studies and a greater focus on clinical trials. Early drug discovery projects have also shifted from phenotypic screens to a molecular target-based and systems approach.
The Productivity-Cost Paradox in Drug Discovery
R&D productivity remains a determining factor of pharmaceutical company success. However, low success rates, a minimum timeline of 10 years until drug approval, and increasingly strict federal regulations have led to significant challenges for maintaining R&D productivity.
The productivity crisis within the pharmaceutical industry is further complicated by enormous R&D costs, which, when coupled with diminishing returns on capital investment, have intensified calls for new operating models. These include an asset-integrating pharma company (AIPCO), which outsources its novel drugs and technologies from external sources. Pfizer, Johnson & Johnson, and AbbVie are notable examples of companies that have adopted the AIPCO model through their continuous acquisition of product innovations developed initially by other companies1.
Scientific Bottlenecks and Strategic Investment Shifts
Between 1980 and 2010, the number of drugs approved by the United States Food and Drug Administration (FDA) remained relatively constant despite significant technological developments within the pharmaceutical industry. As a result, the public has speculated whether the pharmaceutical industry has been experiencing an innovation crisis for the past several decades.
The knowledge necessary for the development of drugs is still very incomplete, despite the scientific advances of the last decades.2
Factors contributing to the innovation crisis within the pharmaceutical industry can be classified as scientific or technological, regulatory, management, or industrial. For example, pharmaceutical companies have access to more information than at other points in history; however, many companies are encountering difficulties exploiting and transforming these advances into new and effective medications.
Likewise, research in new disciplines like proteomics, metabolomics, transcriptomics, and microbiomics is all in their infancy, limiting pharmaceutical companies' ability to incorporate these data into their drug discovery projects. The rapid emergence of highly sensitive technologies, including gene editing, adds opportunities and pressures. They expand therapeutic possibilities while highlighting persistent scientific uncertainties, translational gaps noted across biotechnology, and practical questions of equitable access and affordability.2,3,4,5
Drug Pricing, Access, and Regulatory Pressures
External economic and policy pressures also impact the rate at which new drugs are brought to market. For example, government pressure for pharmaceutical industries to develop cheaper and more generic drugs may discourage new investments into innovative R&D projects, compromising patient access to innovative and potentially more effective treatments in the future3.
Policymakers should consider not only the economic impact of drug price regulation mechanisms, but also their potential long-term effects on pharmaceutical innovation, drug availability, and patient access.3
To date, average prescription drug prices in the U.S. are more than 250% of those in comparable Organization for Economic Co-operation and Development (OECD) nations, which increases the financial burden for much of the U.S. population, particularly seniors and low-income consumers. To address these challenges, several policy changes have been proposed to reduce drug prices in the U.S., including the Inflation Reduction Act (IRA) of 2022, which allows the federal government to negotiate with drug companies to determine the price that Medicare pays for 10 prescription drugs5.
Paths to Resilience and Reinvention
Inequalities in low-, middle-, and high-income countries create significant disparities in patient access to medications. Some of the different socioeconomic factors that influence drug affordability and access, and perpetuate these disparities include income level, geographic location, age, and health status3.
Improving pharmaceutical price transparency is believed to expand access to novel medications while simultaneously reducing their high costs. Several mechanisms provide these benefits, including empowered buyers, increased accountability for both manufacturers and governments, reduced corruption, and enabling cost-effective decision-making by consumers and policymakers. Nevertheless, additional research is needed to highlight the differential impact of policies that promote price transparency on medication prices and downstream investments in R&D.
Recent advances in digital technologies, particularly artificial intelligence (AI) and machine learning, accelerate the early-stage discovery pipeline while optimizing clinical trial designs. More specifically, generative AI models have been trained on massive datasets on chemical structures, biological activity data, pharmacokinetic properties, and toxicity profiles to investigate the structural relationship between targets and their biological function. These advancements can reduce the time and cost traditionally associated with drug development to improve R&D productivity, enhance decision-making processes, and foster innovation across the biopharmaceutical industry.
However, integrating emerging technologies such as AI and next-generation therapies raises ethical considerations, particularly around responsible data use in R&D, as these tools become embedded in decision-making, and around equity of access and affordability when breakthroughs reach the market.1,3,4,5
References
- Schuhmacher, A. (2024). Pharma innovation: how evolutionary economics shapes the future of pharma R&D. Drug Discovery Today 29(12). doi:10.1016/j.drudis.2024.104222.
- Laermann-Nguyen, U., & Backfisch, M. (2021). Innovation crisis in the pharmaceutical industry? A survey. Springer Nature Business & Economics 1(164). doi:10.1007/s43546-021-00163-5.
- Shiwaju, B. I., Orikpete, O. F., Alae, E. Y., et al. (2023). Impact of Drug Price Regulation on Patient Access to Medicines: A Systematic Review. Matrix Science Pharma 7(4); 112-118. doi:10.4103/mtsp.mtsp_23_23.
- Joose, I. R., Tordrup, D., Glanville, J., et al. (2023). Evidence on the effectiveness of policies promoting price transparency – A systematic review. Health Policy. doi:10.1016/j.healthpol.2022.11.002.
- Ho, K., & Pakes, A. (2025). Policy options for the drug pricing conundrum. PNAS 122(9). doi:10.1073/pnas.2418540122.