Why is it hard to terminate failing projects in pharmaceutical R&D?

Comparative analysis of FDA approvals by top 20 pharma companies (2014-2023)

This article addresses the research and development (R&D) productivity challenge of the pharmaceutical industry, focusing on United States Food and Drug Administration (FDA)-related new drug approvals of the top 20 pharmaceutical companies (2014-2023). We evaluated the degree of innovation in new drugs to determine the innovativeness of these leading companies. A key finding of our analysis is the decline in the number of new drugs approved by the FDA for these leading companies over the investigated time period. This trend suggests that some of the leading companies are losing ground in R&D innovation, raising concerns about their ability to sustain competitive advantage, ensure long-term market success, and maintain viable business models.

The current landscape of antifibrotic therapy across different organs: A systematic approach

Fibrosis is a common pathological process that can affect virtually all the organs, but there are hardly any effective therapeutic options. This has led to an intense search for antifibrotic therapies over the last decades, with a great number of clinical assays currently underway. We have systematically reviewed all current and recently finished clinical trials involved in the development of new antifibrotic drugs, and the preclinical studies analyzing the relevance of each of these pharmacological strategies in fibrotic processes affecting tissues beyond those being clinically studied. We analyze and discuss this information with the aim of determining the most promising options and the feasibility of extending their therapeutic value as antifibrotic agents to other fibrotic conditions.

Computational Prediction of Drug Phenotypic Effects Based on Substructure-Phenotype Associations

Identifying drug phenotypic effects, including therapeutic effects and adverse drug reactions (ADRs), is an inseparable part for evaluating the potentiality of new drug candidates (NDCs). However, current computational methods for predicting phenotypic effects of NDCs are mainly based on the overall structure of an NDC or a related target. These approaches often lead to inconsistencies between the structures and functions and limit the prediction space of NDCs. In this study, first, we constructed quantitative associations of substructure-domain, domain-ADR, and domain-ATC (Anatomical Therapeutic Chemical Classification System code) through L1LOG and L1SVM machine learning models. These associations represent relationships between phenotypes (ADRs and ATCs) and local structures of drugs and proteins. Then, based on these established associations, substructure-phenotype relationships were constructed which were utilized to quantify drug-phenotype relationships. Thus, this approach could achieve high-throughput and effective evaluations of the druggability of NDCs by referring to the established substructure-phenotype relationships and structural information of NDCs without additional prior knowledge. Using this computational pipeline, 83,205 drug-ATC relationships (including 1,479 drugs and 178 ATCs) and 306,421 drug-ADR relationships (including 1,752 drugs and 454 ADRs) were predicted in total. The prediction results were validated at four levels: five-fold cross validation, public databases, literature, and molecular docking. Furthermore, three case studies demonstrated the feasibility of our method. 79 ATCs and 269 ADRs were predicted to be related to Maraviroc, an approved drug, including the existing antiviral effect in clinical use. Additionally, we also found risk substructures of severe ADRs, for example, SUB215 (>= 1, saturated or only aromatic carbon ring size 7) can result in shock. And we analyzed the mechanism of action (MOA) of interested drugs based on the established drug-substructure-domain-protein associations. In a word, this approach through establishing drug-substructure-phenotype relationships can achieve quantitative prediction of phenotypes for a given NDC or drug without any prior knowledge except its structure information. Using that way, we can directly obtain the relationships between substructure and phenotype of a compound, which is more convenient to analyze the phenotypic mechanism of drugs and accelerate the process of rational drug design.

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.

Predictive validity in drug discovery: what it is, why it matters and how to improve it

Successful drug discovery is like finding oases of safety and efficacy in chemical and biological deserts. Screens in disease models, and other decision tools used in drug research and development (R&D), point towards oases when they score therapeutic candidates in a way that correlates with clinical utility in humans. Otherwise, they probably lead in the wrong direction. This line of thought can be quantified by using decision theory, in which 'predictive validity' is the correlation coefficient between the output of a decision tool and clinical utility across therapeutic candidates. Analyses based on this approach reveal that the detectability of good candidates is extremely sensitive to predictive validity, because the deserts are big and oases small. Both history and decision theory suggest that predictive validity is under-managed in drug R&D, not least because it is so hard to measure before projects succeed or fail later in the process. This article explains the influence of predictive validity on R&D productivity and discusses methods to evaluate and improve it, with the aim of supporting the application of more effective decision tools and catalysing investment in their creation.

Practical guidance to advisors of residents on the fellowship selection process

Inability to Match into a fellowship is usually not a reflection of some failure on the part of the resident, but rather a problem of supply and demand. Understanding how to advise residents to maximize their success in an environment with limited spots and limited fellowship faculty resources to perform holistic review remains one of the primary objectives of most residency mentors. Residents can alter the odds in their favor by engaging with local faculty and in national society mentorship programs, performing ‘enough’ research, building their ‘brand,’ and concentrating on high quality personal statements.

R&D efficiency of leading pharmaceutical companies - A 20-year analysis

Comparative analysis of the R&D efficiency of 14 leading pharmaceutical companies for the years 1999-2018 shows that there is a close positive correlation between R&D spending and the two investigated R&D output parameters, approved NMEs and the cumulative impact factor of their publications. In other words, higher R&D investments (input) were associated with higher R&D output. Second, our analyses indicate that there are 'economies of scale' (size) in pharmaceutical R&D.

Reviving an R&D pipeline: a step change in the Phase II success rate

The pharmaceutical industry has faced declining research and development (R&D) productivity for decades. During the early 2010s, Pfizer saw its R&D productivity drop even more sharply than did its industry peers. As impactful medicines the company had developed and brought to patients in previous years lost exclusivity, Pfizer faced a steep patent cliff with a cumulative revenue impact of >US$28 billion through 2018. Since 2010, the company has embarked on a focused turnaround effort to improve R&D productivity. Although some efforts will need more time to prove themselves, there are early signs of a turnaround now, particularly in terms of Phase II success rates. Here, we share some learnings from a decade of experience as one of the largest R&D organizations in the industry.

Phase 0/microdosing approaches: time for mainstream application in drug development?

Phase 0 approaches - which include microdosing - evaluate subtherapeutic exposures of new drugs in first-in-human studies known as exploratory clinical trials. Recent progress extends phase 0 benefits beyond assessment of pharmacokinetics to include understanding of mechanism of action and pharmacodynamics. Phase 0 approaches have the potential to improve preclinical candidate selection and enable safer, cheaper, quicker and more informed developmental decisions. Here, we discuss phase 0 methods and applications, highlight their advantages over traditional strategies and address concerns related to extrapolation and developmental timelines. Although challenges remain, we propose that phase 0 approaches be at least considered for application in most drug development scenarios.

Schrödinger's pipeline and the outsourcing of pharmaceutical innovation

In the wake of the Global Financial Crisis (2007-2008) cheaper, softer money flooded the worldwide markets. Faced with historically low capital costs, the pharmaceutical industry chose to pay down debt through share buybacks rather than invest in research and development (R&D). Instead, the industry explored new R&D models for open innovation, such as open-sourcing, crowd-sourcing, public-private partnerships, innovation centres, Science Parks, and the wholesale outsourcing of pharmaceutical R&D. However, economic Greater Fool Theory suggests that outsourcing R&D was never likely to increase innovation. Ten years on, the period of cheaper and softer money is coming to an end. So how are things looking? And what happens next?

Careers in nanomedicine and drug delivery

Nanomedicine continues to be a rapidly growing and increasingly interdisciplinary field. The career opportunities available in nanomedicine are also numerous, yet not always obvious to the early-career scientist determining their individual track for maximal impact. This perspective provides a brief overview of the field of nanomedicine, then delves into the many career trajectories one could take in this field. The article concludes with thoughts on how to provide diverse training to increase supply for the variety of career paths, and the role that mentors can play in young scientists' development and exploration of these career paths.

Drug discovery effectiveness from the standpoint of therapeutic mechanisms and indications

The productivity of the pharmaceutical industry has been widely discussed in recent years, particularly with regard to concerns that substantial expenditures on research and development have failed to translate into approved drugs. Various analyses of this productivity challenge have focused on aspects such as attrition rates at particular clinical phases or the physicochemical properties of drug candidates, but relatively little attention has been paid to how the industry has performed from the standpoint of the choice of therapeutic mechanisms and their intended indications. This article examines what the pharmaceutical industry has achieved in this respect by analysing comprehensive industry-wide data on the mechanism-indication pairs that have been investigated during the past 20 years. Our findings indicate several points and trends that we hope will be useful in understanding and improving the productivity of the industry, including areas in which the industry has had substantial success or failure and the relative extent of novelty in completed and ongoing projects.

Target specificity, in vivo pharmacokinetics, and efficacy of the putative STAT3 inhibitor LY5 in osteosarcoma, Ewing's sarcoma, and rhabdomyosarcoma

Background

STAT3 is a transcription factor involved in cytokine and receptor kinase signal transduction that is aberrantly activated in a variety of sarcomas, promoting metastasis and chemotherapy resistance. The purpose of this work was to develop and test a novel putative STAT3 inhibitor, LY5.

Methods and findings

An in silico fragment-based drug design strategy was used to create LY5, a small molecule inhibitor that blocks the STAT3 SH2 domain phosphotyrosine binding site, inhibiting homodimerization. LY5 was evaluated in vitro demonstrating good biologic activity against rhabdomyosarcoma, osteosarcoma and Ewing’s sarcoma cell lines at high nanomolar/low micromolar concentrations, as well as specific inhibition of STAT3 phosphorylation without effects on other STAT3 family members. LY5 exhibited excellent oral bioavailability in both mice and healthy dogs, and drug absorption was enhanced in the fasted state with tolerable dosing in mice at 40 mg/kg BID. However, RNAi-mediated knockdown of STAT3 did not phenocopy the biologic effects of LY5 in sarcoma cell lines. Moreover, concentrations needed to inhibit ex vivo metastasis growth using the PuMA assay were significantly higher than those needed to inhibit STAT3 phosphorylation in vitro. Lastly, LY5 treatment did not inhibit the growth of sarcoma xenografts or prevent pulmonary metastasis in mice.

Conclusions

LY5 is a novel small molecule inhibitor that effectively inhibits STAT3 phosphorylation and cell proliferation at nanomolar concentrations. LY5 demonstrates good oral bioavailability in mice and dogs. However LY5 did not decrease tumor growth in xenograft mouse models and STAT3 knockdown did not induce concordant biologic effects. These data suggest that the anti-cancer effects of LY5 identified in vitro were not mediated through STAT3 inhibition.

Opportunities and Challenges for Drug Development: Public-Private Partnerships, Adaptive Designs and Big Data

Drug development faces the double challenge of increasing costs and increasing pressure on pricing. To avoid that lack of perceived commercial perspective will leave existing medical needs unmet, pharmaceutical companies and many other stakeholders are discussing ways to improve the efficiency of drug Research and Development. Based on an international symposium organized by the Medical School of the University of Duisburg-Essen (Germany) and held in January 2016, we discuss the opportunities and challenges of three specific areas, i.e., public–private partnerships, adaptive designs and big data. Public–private partnerships come in many different forms with regard to scope, duration and type and number of participants. They range from project-specific collaborations to strategic alliances to large multi-party consortia. Each of them offers specific opportunities and faces distinct challenges. Among types of collaboration, investigator-initiated studies are becoming increasingly popular but have legal, ethical, and financial implications. Adaptive trial designs are also increasingly discussed. However, adaptive should not be used as euphemism for the repurposing of a failed trial; rather it requires carefully planning and specification before a trial starts. Adaptive licensing can be a counter-part of adaptive trial design. The use of Big Data is another opportunity to leverage existing information into knowledge useable for drug discovery and development. Respecting limitations of informed consent and privacy is a key challenge in the use of Big Data. Speakers and participants at the symposium were convinced that appropriate use of the above new options may indeed help to increase the efficiency of future drug development.

Complementary Approaches to Existing Target Based Drug Discovery for Identifying Novel Drug Targets

In the past decade, it was observed that the relationship between the emerging New Molecular Entities and the quantum of R&D investment has not been favorable. There might be numerous reasons but few studies stress the introduction of target based drug discovery approach as one of the factors. Although a number of drugs have been developed with an emphasis on a single protein target, yet identification of valid target is complex. The approach focuses on an in vitro single target, which overlooks the complexity of cell and makes process of validation drug targets uncertain. Thus, it is imperative to search for alternatives rather than looking at success stories of target-based drug discovery. It would be beneficial if the drugs were developed to target multiple components. New approaches like reverse engineering and translational research need to take into account both system and target-based approach. This review evaluates the strengths and limitations of known drug discovery approaches and proposes alternative approaches for increasing efficiency against treatment.

Applying the best of oncology drug development paradigms to the non-malignant space

With 80-90% of drugs entering the clinic not meeting regulatory approval (a high cost of failure), there is a major need for innovation in the clinical development space. Features of the new era of practice-changing innovation in oncology have included novel clinical trial designs incorporating multiple new molecular entities and/or multiple patient types, patient selection strategies (which allow detection of early signs of efficacy), and use of surrogate endpoints to achieve speedy regulatory approval. Disease areas beyond oncology could benefit from the application of specific aspects of these approaches. Here, we describe several such potential adaptations of the approaches, with scenarios and prerequisites, which could help reduce the costs of, and accelerate, clinical drug development with confidence.

Molecular inflation, attrition and the rule of five

Physicochemical properties underlie all aspects of drug action and are critical for solubility, permeability and successful formulation. Specific physicochemical properties shown to be relevant to oral drugs are size, lipophilicity, ionisation, hydrogen bonding, polarity, aromaticity and shape. The rule of 5 (Ro5) and subsequent studies have raised awareness of the importance of compound quality amongst bioactive molecules. Lipophilicity, probably the most important physical property of oral drugs, has on average changed little over time in oral drugs, until increases in drugs published after 1990. In contrast other molecular properties such as average size have increased significantly. Factors influencing property inflation include the targets pursued, where antivirals frequently violate the Ro5, risk/benefit considerations, and variable drug discovery practices. The compounds published in patents from the pharmaceutical industry are on average larger, more lipophilic and less complex than marketed oral drugs. The variation between individual companies' patented compounds is due to different practices and not to the targets pursued. Overall, there is demonstrable physical property attrition in moving from patents to candidate drugs to marketed drugs. The pharmaceutical industry's recent poor productivity has been due, in part, to progression of molecules that are unable to unambiguously test clinical efficacy, and attrition can therefore be improved by ensuring candidate drug quality is 'fit for purpose.' The combined ligand efficiency (LE) and lipophilic ligand efficiency (LLE) values of many marketed drugs are optimised relative to other molecules acting at the same target. Application of LLE in optimisation can help identify improved leads, even with challenging targets that seem to require lipophilic ligands. Because of their targets, some projects may need to pursue 'beyond Ro5' physicochemical space; such projects will require non-standard lead generation and optimisation and should not dominate in a well-balanced portfolio. Compound quality is controllable by lead selection and optimisation and should not be a cause of clinical failure.