Pharma’s potential breakthroughs in AI, ADCs, and GLP-1 receptor agonists raise a critical question: can innovation outpace the relentless rise of chronic disease?
The IQVIA Institute for Human Data Science sheds light on this theme, among many others, in its 80-page Global Trends in R&D 2024 report.
Pillar 1: GLP-1 receptor agonists targeting metabolic disease
Speaking of next-gen metabolic therapies in particular, Murray Aitken, the executive director of the IQVIA Institute for Human Data Science, sees significant potential. Market projections for GLP-1 drugs are bullish, with some analysts projecting sales potentially hitting $100 billion by 2030. “It’s exciting because if the market for these drugs becomes as big as anticipated, it means they are truly being disruptive in a positive way to human health for hundreds of millions of people worldwide,” said Aitken
New metabolic therapies are sorely needed as the obesity epidemic continues to drive chronic disease rates, with one billion adults projected to be obese by 2025 and nearly half of Americans obese by 2030. Already, more than one in three American adults have prediabetes. These alarming statistics underscore the critical need to address the consequences of metabolic disorders. Obesity alone is a major contributor to heart disease, stroke, cancer, diabetes, and arthritis — key causes of death and disability. In the U.S., obesity-related healthcare spending exceeds 20% annually. Additionally, diabetes is implicated in a range of serious health complications that can affect nearly every organ in the body if the condition is not well-managed.
Continuing to shift the standard of care in metabolic disease
This QR code redirects to the Global Trends in R&D 2024 report.
GLP-1 receptor agonists like semaglutide and tirzepatide (a dual GLP-1/GIP agonist) show results in weight loss and metabolic improvement with little precedent. Aitken believes these drugs could transform the treatment of obesity-related diseases, noting the surge in drug development spurred by their compelling clinical data. “It’s not surprising that there’s now a pipeline of over 120 drugs in active development coming along,” Aitken said.
Heavy pharma investment in GLP-1 receptor agonists (35% of obesity drugs in development) highlights their potential to shift the standard of care for a range of diseases. This focus mirrors compelling clinical data and the drug class’s potential to ease the healthcare burden caused by obesity. The development of oral formulations expands on this potential, with 46% of obesity drugs in development being oral. “There’s always been a preference in some parts of the population for oral formulations over injectables.” Aitken said.
Pillar 2: ADCs, the precision weapons of cancer therapy
Similar to how GLP-1 receptor agonists are upending the treatment for some patients with metabolic disease, antibody-drug conjugates (ADCs) are having a similar impact on cancer treatment. The therapies merge the specificity of monoclonal antibodies with potent cytotoxic drugs to precisely target tumor cells while sparing healthy tissue. Advances in linker technology and more selective cytotoxic agents are helping fuel the trend. “ADCs have become a hotspot in oncology now representing 9% of the oncology pipeline,” Aitken said.
ADCs’ ascent signifies a shift from the previously dominant programmed cell death protein 1/programmed death-ligand 1 (PD-1/PD-L1) inhibitors. The regulatory landscape has embraced ADCs, with approvals like Enhertu (trastuzumab deruxtecan) and Trodelvy (sacituzumab govitecan-hziy) marking them as vital therapies.
Murray Aitken is the executive director of the IQVIA Institute for Human Data Science
One element driving progress in the field is the international dynamic of ADC development, involving companies across the U.S., Europe, and Asia, including China. “We saw a lot of the ADCs that were licensed last year that came from China-based companies,” Aitken said. “That suggests that, in China, the activity has reached a high maturity level where the drugs are attracting interest from multinational companies looking to fill their pipelines.”
In the U.S., companies like Seattle Genetics (SeaGen, acquired by Pfizer in 2023) stand out as pioneers, while European companies contribute significantly to new cytotoxic agents, linker technologies, and manufacturing capabilities. With hundreds of companies globally engaged in ADC development and application – spanning technology vendors, established pharmaceutical companies, and up-and-coming startups – the landscape is both competitive and diverse.
Pillar 3: AI promises to accelerate drug discovery
AI and machine learning (ML) are no longer buzzwords in the pharma industry – they’re now becoming fundamental drug discovery and development tools with a host of uses. AI’s ability to analyze vast and complex datasets – from chemical structures to patient records – is accelerating the identification of potential drug targets and the design of optimized drug candidates and trials.
“We are definitely seeing a lot of interest in applying AI to discovery research. We’re still at the beginning, but we anticipate AI having a transformative impact on discovery research,” Aitken added. Over time, the technology could translate into “a greater number of identified druggable targets, faster throughput from the discovery stage into preclinical and potentially with a higher probability of scientific success as those AI-developed, AI-discovered molecules.”
Core AI applications in drug development
AI is reshaping drug discovery, with core applications including(see Figure 61 of the IQVIA Institute for Human Data Science report):
- Target Identification (33%): AI can help researchers sift through massive amounts of clinical, experimental, and ‘omics’ data to uncover the mechanisms of diseases. Aitken notes AI’s particular value in “applying AI to very large and cell-based datasets that increasingly exist and are accessible” to facilitate the identification of promising new targets.
- Drug Design (42%): AI algorithms excel at analyzing complex molecular structures, predicting interactions, and optimizing drug candidates, making it the most prominent current use of AI in the drug discovery process.
- Precision Medicine (17%): AI is used to analyze patient data, including genomics and biomarkers. This allows researchers to tailor drug development and clinical trials for specific patient populations to improve treatment outcomes.
- Trial Simulation (8%): AI-powered simulations can predict trial outcomes and optimize clinical trial design. AI can be impactful in identifying sites and finding patients who meet the criteria for enrollment, thus shortening the enrollment period. This saves time and resources by identifying potential issues early in the process.
While AI and data science in drug discovery offer considerable promise, the disciplines are not a replacement for sound leadership and insight. “Data science is wonderful, but it only delivers value when a human makes a different decision or takes a different action based on what’s coming out of it,” Aitken said.“And I think that sometimes gets lost, particularly when we get excited about data science. You need data science, but you also need humans connected to it.”