Costs and Causes of Oncology Drug Attrition With the Example of Insulin-Like Growth Factor-1 Receptor Inhibitors.

Importance: The development of oncology drugs is expensive and beset by a high attrition rate. Analysis of the costs and causes of translational failure may help to reduce attrition and permit the more appropriate use of resources to reduce mortality from cancer. Objective: To analyze the causes of failure and expenses incurred in clinical trials of novel oncology drugs, with the example of insulin-like growth factor-1 receptor (IGF-1R) inhibitors, none of which was approved for use in oncology practice. Design, Setting, and Participants: In this cross-sectional study, inhibitors of the IGF-1R and their clinical trials for use in oncology practice between January 1, 2000, and July 31, 2021, were identified by searching PubMed and ClinicalTrials.gov. A proprietary commercial database was interrogated to provide expenses incurred in these trials. If data were not available, estimates were made of expenses using mean values from the proprietary database. A search revealed studies of the effects of IGF-1R inhibitors in preclinical in vivo assays, permitting calculation of the percentage of tumor growth inhibition. Archival data on the clinical trials of IGF-1R inhibitors and proprietary estimates of their expenses were examined, together with an analysis of preclinical data on IGF-1R inhibitors obtained from the published literature. Main Outcomes and Measures: Expenses associated with research and development of IGF-1R inhibitors. Results: Sixteen inhibitors of IGF-1R studied in 183 clinical trials were found. None of the trials, in a wide range of tumor types, showed efficacy permitting drug approval. More than 12 000 patients entered trials of IGF-1R inhibitors in oncology indications in 2003 to 2021. These trials incurred aggregate research and development expenses estimated at between $1.6 billion and $2.3 billion. Analysis of the results of preclinical in vivo assays of IGF-1R inhibitors that supported subsequent clinical investigations showed mixed activity and protocols that poorly reflected the treatment of advanced metastatic tumors in humans. Conclusions and Relevance: Failed drug development in oncology incurs substantial expense. At an industry level, an estimated $50 billion to $60 billion is spent annually on failed oncology trials. Improved target validation and more appropriate preclinical models are required to reduce attrition, with more attention to decision-making before launching clinical trials. A more appropriate use of resources may better reduce cancer mortality.

Performance assessment and economic analysis of a human Liver-Chip for predictive toxicology.

BACKGROUND: Conventional preclinical models often miss drug toxicities, meaning the harm these drugs pose to humans is only realized in clinical trials or when they make it to market. This has caused the pharmaceutical industry to waste considerable time and resources developing drugs destined to fail. Organ-on-a-Chip technology has the potential improve success in drug development pipelines, as it can recapitulate organ-level pathophysiology and clinical responses; however, systematic and quantitative evaluations of Organ-Chips' predictive value have not yet been reported. METHODS: 870 Liver-Chips were analyzed to determine their ability to predict drug-induced liver injury caused by small molecules identified as benchmarks by the Innovation and Quality consortium, who has published guidelines defining criteria for qualifying preclinical models. An economic analysis was also performed to measure the value Liver-Chips could offer if they were broadly adopted in supporting toxicity-related decisions as part of preclinical development workflows. RESULTS: Here, we show that the Liver-Chip met the qualification guidelines across a blinded set of 27 known hepatotoxic and non-toxic drugs with a sensitivity of 87% and a specificity of 100%. We also show that this level of performance could generate over $3 billion annually for the pharmaceutical industry through increased small-molecule R&D productivity. CONCLUSIONS: The results of this study show how incorporating predictive Organ-Chips into drug development workflows could substantially improve drug discovery and development, allowing manufacturers to bring safer, more effective medicines to market in less time and at lower costs.

Drug development is lengthy and costly, as it relies on laboratory models that fail to predict human reactions to potential drugs. Because of this, toxic drugs sometimes go on to harm humans when they reach clinical trials or once they are in the marketplace. Organ-on-a-Chip technology involves growing cells on small devices to mimic organs of the body, such as the liver. Organ-Chips could potentially help identify toxicities earlier, but there is limited research into how well they predict these effects compared to conventional models. In this study, we analyzed 870 Liver-Chips to determine how well they predict drug-induced liver injury, a common cause of drug failure, and found that Liver-Chips outperformed conventional models. These results suggest that widespread acceptance of Organ-Chips could decrease drug attrition, help minimize harm to patients, and generate billions in revenue for the pharmaceutical industry.

Biopharmaceutical R&D outsourcing: Short-term gain for long-term pain?

Research and development (R&D) outsourcing offers some obvious productivity benefits (e.g., access to new technology, variabilised costs, risk sharing, etc.). However, recent work in economics points to a productivity headwind at the level of the innovation ecosystem. The market for technologies with economies of scope and knowledge spillovers (those with the biggest impact on industry economics and social welfare) has structural features that allow customers to capture a disproportionate share of economic value and transfer a disproportionate share of economic risk to technology providers, even though the providers aim to maximise profit. This reduces the incentives to invest in new ventures that specialise in the most promising early-stage projects. Therefore, near-term gains from R&D outsourcing can be offset by slower innovation in the long run.

Evidence for the effectiveness of minimum pricing of alcohol: a systematic review and assessment using the Bradford Hill criteria for causality.

OBJECTIVES: To assess the evidence for price-based alcohol policy interventions to determine whether minimum unit pricing (MUP) is likely to be effective. DESIGN: Systematic review and assessment of studies according to Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines, against the Bradford Hill criteria for causality. Three electronic databases were searched from inception to February 2017. Additional articles were found through hand searching and grey literature searches. CRITERIA FOR SELECTING STUDIES: We included any study design that reported on the effect of price-based interventions on alcohol consumption or alcohol-related morbidity, mortality and wider harms. Studies reporting on the effects of taxation or affordability and studies that only investigated price elasticity of demand were beyond the scope of this review. Studies with any conflict of interest were excluded. All studies were appraised for methodological quality. RESULTS: Of 517 studies assessed, 33 studies were included: 26 peer-reviewed research studies and seven from the grey literature. All nine of the Bradford Hill criteria were met, although different types of study satisfied different criteria. For example, modelling studies complied with the consistency and specificity criteria, time series analyses demonstrated the temporality and experiment criteria, and the analogy criterion was fulfilled by comparing the findings with the wider literature on taxation and affordability. CONCLUSIONS: Overall, the Bradford Hill criteria for causality were satisfied. There was very little evidence that minimum alcohol prices are not associated with consumption or subsequent harms. However the overall quality of the evidence was variable, a large proportion of the evidence base has been produced by a small number of research teams, and the quantitative uncertainty in many estimates or forecasts is often poorly communicated outside the academic literature. Nonetheless, price-based alcohol policy interventions such as MUP are likely to reduce alcohol consumption, alcohol-related morbidity and mortality.

When Quality Beats Quantity: Decision Theory, Drug Discovery, and the Reproducibility Crisis.

A striking contrast runs through the last 60 years of biopharmaceutical discovery, research, and development. Huge scientific and technological gains should have increased the quality of academic science and raised industrial R&D efficiency. However, academia faces a "reproducibility crisis"; inflation-adjusted industrial R&D costs per novel drug increased nearly 100 fold between 1950 and 2010; and drugs are more likely to fail in clinical development today than in the 1970s. The contrast is explicable only if powerful headwinds reversed the gains and/or if many "gains" have proved illusory. However, discussions of reproducibility and R&D productivity rarely address this point explicitly. The main objectives of the primary research in this paper are: (a) to provide quantitatively and historically plausible explanations of the contrast; and (b) identify factors to which R&D efficiency is sensitive. We present a quantitative decision-theoretic model of the R&D process. The model represents therapeutic candidates (e.g., putative drug targets, molecules in a screening library, etc.) within a "measurement space", with candidates' positions determined by their performance on a variety of assays (e.g., binding affinity, toxicity, in vivo efficacy, etc.) whose results correlate to a greater or lesser degree. We apply decision rules to segment the space, and assess the probability of correct R&D decisions. We find that when searching for rare positives (e.g., candidates that will successfully complete clinical development), changes in the predictive validity of screening and disease models that many people working in drug discovery would regard as small and/or unknowable (i.e., an 0.1 absolute change in correlation coefficient between model output and clinical outcomes in man) can offset large (e.g., 10 fold, even 100 fold) changes in models' brute-force efficiency. We also show how validity and reproducibility correlate across a population of simulated screening and disease models. We hypothesize that screening and disease models with high predictive validity are more likely to yield good answers and good treatments, so tend to render themselves and their diseases academically and commercially redundant. Perhaps there has also been too much enthusiasm for reductionist molecular models which have insufficient predictive validity. Thus we hypothesize that the average predictive validity of the stock of academically and industrially "interesting" screening and disease models has declined over time, with even small falls able to offset large gains in scientific knowledge and brute-force efficiency. The rate of creation of valid screening and disease models may be the major constraint on R&D productivity.

Diagnosing the decline in pharmaceutical R&D efficiency.

The past 60 years have seen huge advances in many of the scientific, technological and managerial factors that should tend to raise the efficiency of commercial drug research and development (RD). Yet the number of new drugs approved per billion US dollars spent on RD has halved roughly every 9 years since 1950, falling around 80-fold in inflation-adjusted terms. There have been many proposed solutions to the problem of declining RD efficiency. However, their apparent lack of impact so far and the contrast between improving inputs and declining output in terms of the number of new drugs make it sensible to ask whether the underlying problems have been correctly diagnosed. Here, we discuss four factors that we consider to be primary causes, which we call the 'better than the Beatles' problem; the 'cautious regulator' problem; the 'throw money at it' tendency; and the 'basic research-brute force' bias. Our aim is to provoke a more systematic analysis of the causes of the decline in RD efficiency.