Analysis of pharma R&D productivity - a new perspective needed

Machine Learning for Toxicity Prediction Using Chemical Structures: Pillars for Success in the Real World

Machine learning (ML) is increasingly valuable for predicting molecular properties and toxicity in drug discovery. However, toxicity-related end points have always been challenging to evaluate experimentally with respect to in vivo translation due to the required resources for human and animal studies; this has impacted data availability in the field. ML can augment or even potentially replace traditional experimental processes depending on the project phase and specific goals of the prediction. For instance, models can be used to select promising compounds for on-target effects or to deselect those with undesirable characteristics (e.g., off-target or ineffective due to unfavorable pharmacokinetics). However, reliance on ML is not without risks, due to biases stemming from nonrepresentative training data, incompatible choice of algorithm to represent the underlying data, or poor model building and validation approaches. This might lead to inaccurate predictions, misinterpretation of the confidence in ML predictions, and ultimately suboptimal decision-making. Hence, understanding the predictive validity of ML models is of utmost importance to enable faster drug development timelines while improving the quality of decisions. This perspective emphasizes the need to enhance the understanding and application of machine learning models in drug discovery, focusing on well-defined data sets for toxicity prediction based on small molecule structures. We focus on five crucial pillars for success with ML-driven molecular property and toxicity prediction: (1) data set selection, (2) structural representations, (3) model algorithm, (4) model validation, and (5) translation of predictions to decision-making. Understanding these key pillars will foster collaboration and coordination between ML researchers and toxicologists, which will help to advance drug discovery and development.

New drugs and their performance 10 years after approval: a systematic analysis

More than 10,000 drugs are available on today's market in Germany, with an increasing number receiving institutional approval every year. However, their general efficacy and utility for patients are often not thoroughly analyzed and, in some cases, remain questionable. The lack of systematic analysis for these parameters hinders learning from failures and successes. As a result, there is a risk of wasting resources by the pharmaceutical industry and approving new drugs that offer no additional benefit to patients in need of innovative treatments. Therefore, we set out to analyze the evolution of new drugs with innovative principles over 10 years after approval by the European Medicines Agency (EMA). We focused on drugs approved from 2004 to 2011, excluding protein kinase inhibitors and monoclonal antibodies, and identified 190 new drugs using the Arzneiverordnungsreport (AVR, Drug prescription report). With data from the Wissenschaftliches Institut der Ortskrankenkassen (WidO, Scientific Institute of the General Local Health Insurance Fund, AOK), we analyzed their number of prescriptions, sales, defined daily doses (DDD), and daily costs. We then extended our analysis with a focus on Rote-Hand-Briefe (RHB, Direct Healthcare Professional Communication). We identified factors of success, which was defined as a drug appearing in the top 3000. We then conducted a detailed analysis of outliers in terms of sales, focusing on parameters such as indications, innovation regarding their mechanism of action, relative costs, competition, pharmacological properties, and clinical studies. The analysis of both the most and least successful, allowed us to identify clear correlations and determine potential red flags as well as green flags regarding pharmaceutical sales. Nearly half (49%) of the drugs analyzed met our success criterion, most very early (66% within the first 2 years). Most of these drugs also showed a notable progression in the drug rankings over the years. Thirty percent of all analyzed drugs received RHBs, with most of them (84% of the said 30%) receiving at least one deemed potentially influential regarding sales. The successful drugs were more often subject to these potentially influential RHBs than their non-successful counterparts, and most of the potentially influential RHBs were related to adverse drug reactions (53%) or indications or contraindications (14.8%). Based on the analysis of the tops and flops, we conclude that market success, measured by sales, is influenced by multiple factors. These include indication(s), innovation, the competitive landscape, costs, and pharmacological aspects as well as studies regarding the efficacy and the adverse drug reactions of the drug. These results underline the necessity of a multifactorial approach based on value-adding to assess potential new drugs by pharmaceutical companies.

Cross-disciplinary perspectives on the potential for artificial intelligence across chemistry

From accelerating simulations and exploring chemical space, to experimental planning and integrating automation within experimental labs, artificial intelligence (AI) is changing the landscape of chemistry. We are seeing a significant increase in the number of publications leveraging these powerful data-driven insights and models to accelerate all aspects of chemical research. For example, how we represent molecules and materials to computer algorithms for predictive and generative models, as well as the physical mechanisms by which we perform experiments in the lab for automation. Here, we present ten diverse perspectives on the impact of AI coming from those with a range of backgrounds from experimental chemistry, computational chemistry, computer science, engineering and across different areas of chemistry, including drug discovery, catalysis, chemical automation, chemical physics, materials chemistry. The ten perspectives presented here cover a range of themes, including AI for computation, facilitating discovery, supporting experiments, and enabling technologies for transformation. We highlight and discuss imminent challenges and ways in which we are redefining problems to accelerate the impact of chemical research via AI.

Computational drug repurposing: approaches, evaluation of in silico resources and case studies

Repurposing of existing drugs for new indications has attracted substantial attention owing to its potential to accelerate drug development and reduce costs. Hundreds of computational resources such as databases and predictive platforms have been developed that can be applied for drug repurposing, making it challenging to select the right resource for a specific drug repurposing project. With the aim of helping to address this challenge, here we overview computational approaches to drug repurposing based on a comprehensive survey of available in silico resources using a purpose-built drug repurposing ontology that classifies the resources into hierarchical categories and provides application-specific information. We also present an expert evaluation of selected resources and three drug repurposing case studies implemented within the Horizon Europe REMEDi4ALL project to demonstrate the practical use of the resources. This comprehensive Review with expert evaluations and case studies provides guidelines and recommendations on the best use of various in silico resources for drug repurposing and establishes a basis for a sustainable and extendable drug repurposing web catalogue.

A case study assessing the impact of M&A and licensing on FDA drug approvals of leading pharmaceutical companies

Despite a recent increase in FDA new drug approvals, leading pharmaceutical companies continue to face R&D productivity challenges. This highlights the need to better understand the context of their R&D concepts and related R&D outputs. Consequently, we conducted a systematic assessment of the impact of R&D expenditures, R&D intensities, mergers & acquisitions (M&A) deals and licensing agreements on new drug approvals of leading pharmaceutical companies between 2012 and 2021. Our analysis provides key insights into differentiating R&D factors: whereas R&D expenditures and the number of M&A deals correlate with the number of new drug approvals, our analysis shows no correlation with R&D intensity or the number of licensing agreements.

Enabling research and development innovation in the life sciences: A case study

Many industries face challenges in driving an innovation strategy that sustains value growth and aligns with the company's social responsibility. Bayer is in a unique position as a global diversified life science company, with a mission to alleviate hunger and improve health. We propose that research-intensive firms such as Bayer must consider an R&D innovation strategy in addition to a typical product innovation strategy to ensure successful short- and long-term R&D-enabled value creation. To this end, we introduce an R&D technology-product-market innovation framework and apply this framework to a case study involving the Life Science Collaboration (LSC) program, Bayer's internal multidisciplinary R&D seed fund. In the light of the innovation framework we propose, we show that the LSC's community-driven decision-making enables broad strategic alignment with business needs while maintaining space for out-of-the-box ideas.

New Drug Expenditure by Therapeutic Area in South Korea: International Comparison and Policy Implications

Background: Pharmaceutical expenditures serve as key indicators of healthcare system efficiency, innovation, and sustainability. South Korea has implemented policies such as the economic evaluation exemption (EEE) and risk-sharing agreements (RSAs) to balance cost control and access to innovative therapies. However, discrepancies persist in the distribution of expenditures across therapeutic areas, raising concerns about alignment with public health needs. Methods: This retrospective observational study analyzed pharmaceutical expenditures in South Korea from 2007 to 2022, focusing on new chemical entities (NCEs). Data sources included the IQVIA MIDAS Global Database, the WHO Global Burden of Disease (GBD) database, and South Korea's national health insurance records. Expenditure patterns were benchmarked against OECD and A8 countries using disability-adjusted life years (DALYs) and other healthcare metrics to assess the relationship between spending and disease burden. Results: By 2022, South Korea had introduced 276 NCEs, demonstrating progress, but still lagging the OECD average. NCE expenditure increased from 10.0% to 16.0% of total pharmaceutical spending between 2017 and 2022, whereas A8 countries' share rose from 26.2% to 48.1%. While oncology expenditures were proportionate to disease burden, spending on chronic diseases such as musculoskeletal and cardiovascular conditions remained relatively low compared to their DALY contributions. Conclusions: Although South Korea has strengthened its investment in pharmaceutical innovation, disparities in expenditure distribution persist. Refining policies to enhance resource allocation for chronic diseases and expanding the RSA framework beyond oncology could improve equity and sustainability. Adopting international best practices-such as indication-based pricing and funding mechanisms for high-cost therapies-may further support optimal pharmaceutical expenditure management.

Philanthropic drug development: understanding its importance, mechanisms, and future prospects

Philanthropic drug development (PDD) addresses gaps in traditional pharmaceutical innovation, particularly for rare and underserved diseases. Cost and timeline challenges discourage new investments, especially in niche therapeutic areas. Patient organizations (POs) are uniquely positioned to help to reduce development challenges by providing expertise, supporting early research, fostering collaborations, and driving patient-centered clinical trials. PDD relies on effective partnerships between POs, pharmaceutical companies, and other stakeholders, ensuring that patient perspectives inform the drug development process. PDD is poised to relieve the pressure on the traditional drug development process and thereby foster beneficial patient-focused innovations. In doing so, PDD allows pharmaceutical companies to expand their drug development activities into commercially unrewarding {} areas, diversifying their portfolios beyond competitive fields.

Benchmarking R&D success rates of leading pharmaceutical companies: an empirical analysis of FDA approvals (2006-2022)

Previous analyses provide an industry benchmark of ∼10% for the success rate in clinical development. However, prior analyses were limited by a narrow timeframe, a diverse research focus, biases in phase-to-phase transition methodology or a focus on specific use cases. We calculated unbiased input:output ratios (Phase I to FDA new drug approval) to analyze the likelihood of first approval using data from clinicaltrials.gov, encompassing a total of 2092 active ingredients, 19 927 clinical trials conducted by 18 leading pharmaceutical companies (2006-2022) and 274 new drug approvals. Our study reveals an average likelihood of first approval rate of 14.3% across leading research-based pharmaceutical companies, broadly ranging from 8% to 23%.

Validation guidelines for drug-target prediction methods

INTRODUCTION

Mapping the interactions between pharmaceutical compounds and their molecular targets is a fundamental aspect of drug discovery and repurposing. Drug-target interactions are important for elucidating mechanisms of action and optimizing drug efficacy and safety profiles. Several computational methods have been developed to systematically predict drug-target interactions. However, computational and experimental validation of the drug-target predictions greatly vary across the studies.

AREAS COVERED

Through a PubMed query, a corpus comprising 3,286 articles on drug-target interaction prediction published within the past decade was covered. Natural language processing was used for automated abstract classification to study the evolution of computational methods, validation strategies and performance assessment metrics in the 3,286 articles. Additionally, a manual analysis of 259 studies that performed experimental validation of computational predictions revealed prevalent experimental protocols.

EXPERT OPINION

Starting from 2014, there has been a noticeable increase in articles focusing on drug-target interaction prediction. Docking and regression stands out as the most commonly used techniques among computational methods, and cross-validation is frequently employed as the computational validation strategy. Testing the predictions using multiple, orthogonal validation strategies is recommended and should be reported for the specific target prediction applications. Experimental validation remains relatively rare and should be performed more routinely to evaluate biological relevance of predictions.

Pharma innovation: how evolutionary economics is shaping the future of pharma R&D

This paper describes the theory of evolutionary economics in the context of pharmaceutical R&D. In this context, the R&D productivity crisis acts as a key selection mechanism, and R&D, technology and industry trends provide mechanisms of variation. Drawing on today's prevailing business model among leading pharmaceutical companies, the biotech-leveraged pharma company (BIPCO), I propose two new value creation logics: the technology-investigating pharma company (TIPCO) and the asset-integrating pharma company (AIPCO). Although some companies already share aspects of these business models, it is not yet clear, in terms of evolutionary economics, what the ultimate outcome of the evolutionary process in pharma R&D will be.

The pharmaceutical productivity gap - Incremental decline in R&D efficiency despite transient improvements

Rising research and development costs, currently exceeding $3.5 billion per novel drug, reflect a five-decade decline in pharmaceutical R&D efficiency. While recent reports suggest a potential turnaround, this review offers a systems-level analysis to explore whether this marks a structural shift or transient reversal. We analyzed financial data from the 200 largest pharmaceutical firms, novel drug approvals, and more than 80 000 clinical trials between 2012 and 2023. Our analysis revealed that despite recent stabilization, the pharmaceutical industry continues to face challenges, particularly due to elevated late-stage clinical attrition, suggesting that a sustained turnaround in R&D efficiency remains elusive.

Utilization of phase I studies for target validation of first-in-class drugs

This review discusses the growing importance of target validation within phase I (P1) trials as a new trend in drug development, especially in establishing proof of concept (POC) for first-in-class drugs. The paper describes two approaches: the P1-PIV approach, which directly evaluates the primary endpoint for a pivotal clinical study to confirm therapeutic effects during P1, and the newly introduced P1-FCTE, which assesses functional changes necessary for therapeutic effect as a novel target validation milestone in P1. By providing practical examples of first-in-class drugs, we compare the benefits, costs, hurdles and applicable therapeutic areas of these approaches. Finally, we discuss the potential of these novel approaches to facilitate POC success, shorten development timelines and ultimately increase drug discovery success rates.

Data-centric challenges with the application and adoption of artificial intelligence for drug discovery

INTRODUCTION

Artificial intelligence (AI) is exhibiting tremendous potential to reduce the massive costs and long timescales of drug discovery. There are however important challenges currently limiting the impact and scope of AI models.

AREAS COVERED

In this perspective, the authors discuss a range of data issues (bias, inconsistency, skewness, irrelevance, small size, high dimensionality), how they challenge AI models, and which issue-specific mitigations have been effective. Next, they point out the challenges faced by uncertainty quantification techniques aimed at enhancing and trusting the predictions from these AI models. They also discuss how conceptual errors, unrealistic benchmarks and performance misestimation can confound the evaluation of models and thus their development. Lastly, the authors explain how human bias, whether from AI experts or drug discovery experts, constitutes another challenge that can be alleviated by gaining more prospective experience.

EXPERT OPINION

AI models are often developed to excel on retrospective benchmarks unlikely to anticipate their prospective performance. As a result, only a few of these models are ever reported to have prospective value (e.g. by discovering potent and innovative drug leads for a therapeutic target). The authors have discussed what can go wrong in practice with AI for drug discovery. The authors hope that this will help inform the decisions of editors, funders investors, and researchers working in this area.

Accelerating drug discovery, development, and clinical trials by artificial intelligence

Artificial intelligence (AI) has profoundly advanced the field of biomedical research, which also demonstrates transformative capacity for innovation in drug development. This paper aims to deliver a comprehensive analysis of the progress in AI-assisted drug development, particularly focusing on small molecules, RNA, and antibodies. Moreover, this paper elucidates the current integration of AI methodologies within the industrial drug development framework. This encompasses a detailed examination of the industry-standard drug development process, supplemented by a review of medications presently undergoing clinical trials. Conclusively, the paper tackles a predominant obstacle within the AI pharmaceutical sector: the absence of AI-conceived drugs receiving approval. This paper also advocates for the adoption of large language models and diffusion models as a viable strategy to surmount this challenge. This review not only underscores the significant potential of AI in drug discovery but also deliberates on the challenges and prospects within this dynamically progressing field.

Application of Virtual Drug Study to New Drug Research and Development: Challenges and Opportunity

In recent years, virtual drug study, as an emerging research strategy, has become increasingly important in guiding and promoting new drug research and development. Researchers can integrate a variety of technical methods to improve the efficiency of all phases of new drug research and development, including the use of artificial intelligence, modeling and simulation for target identification, compound screening and pharmacokinetic characteristics evaluation, and the application of clinical trial simulation to carry out clinical research. This paper aims to elaborate on the application of virtual drug study in the key stages of new drug research and development and discuss the opportunities and challenges it faces in supporting new drug research and development.

Attention-based approach to predict drug-target interactions across seven target superfamilies

MOTIVATION

Drug-target interactions (DTIs) hold a pivotal role in drug repurposing and elucidation of drug mechanisms of action. While single-targeted drugs have demonstrated clinical success, they often exhibit limited efficacy against complex diseases, such as cancers, whose development and treatment is dependent on several biological processes. Therefore, a comprehensive understanding of primary, secondary and even inactive targets becomes essential in the quest for effective and safe treatments for cancer and other indications. The human proteome offers over a thousand druggable targets, yet most FDA-approved drugs bind to only a small fraction of these targets.

RESULTS

This study introduces an attention-based method (called as MMAtt-DTA) to predict drug-target bioactivities across human proteins within seven superfamilies. We meticulously examined nine different descriptor sets to identify optimal signature descriptors for predicting novel DTIs. Our testing results demonstrated Spearman correlations exceeding 0.72 (P < 0.001) for six out of seven superfamilies. The proposed method outperformed fourteen state-of-the-art machine learning, deep learning and graph-based methods and maintained relatively high performance for most target superfamilies when tested with independent bioactivity data sources. We computationally validated 185 676 drug-target pairs from ChEMBL-V33 that were not available during model training, achieving a reasonable performance with Spearman correlation >0.57 (P < 0.001) for most superfamilies. This underscores the robustness of the proposed method for predicting novel DTIs. Finally, we applied our method to predict missing bioactivities among 3492 approved molecules in ChEMBL-V33, offering a valuable tool for advancing drug mechanism discovery and repurposing existing drugs for new indications.

AVAILABILITY AND IMPLEMENTATION

https://github.com/AronSchulman/MMAtt-DTA.

Roadblocks confronting widespread dissemination and deployment of Organs on Chips

Organ on Chip platforms hold significant promise as alternatives to animal models or traditional cell cultures, both of which poorly recapitulate human pathophysiology and human level responses. Within the last 15 years, we have witnessed seminal scientific developments from academic laboratories, a flurry of startups and investments, and a genuine interest from pharmaceutical industry as well as regulatory authorities to translate these platforms. This Perspective identifies several fundamental design and process features that may act as roadblocks that prevent widespread dissemination and deployment of these systems, and provides a roadmap to help position this technology in mainstream drug discovery.

DrugGym: A testbed for the economics of autonomous drug discovery

Drug discovery is stochastic. The effectiveness of candidate compounds in satisfying design objectives is unknown ahead of time, and the tools used for prioritization-predictive models and assays-are inaccurate and noisy. In a typical discovery campaign, thousands of compounds may be synthesized and tested before design objectives are achieved, with many others ideated but deprioritized. These challenges are well-documented, but assessing potential remedies has been difficult. We introduce DrugGym, a framework for modeling the stochastic process of drug discovery. Emulating biochemical assays with realistic surrogate models, we simulate the progression from weak hits to sub-micromolar leads with viable ADME. We use this testbed to examine how different ideation, scoring, and decision-making strategies impact statistical measures of utility, such as the probability of program success within predefined budgets and the expected costs to achieve target candidate profile (TCP) goals. We also assess the influence of affinity model inaccuracy, chemical creativity, batch size, and multi-step reasoning. Our findings suggest that reducing affinity model inaccuracy from 2 to 0.5 pIC50 units improves budget-constrained success rates tenfold. DrugGym represents a realistic testbed for machine learning methods applied to the hit-to-lead phase. Source code is available at www.drug-gym.org.

Another string to your bow: machine learning prediction of the pharmacokinetic properties of small molecules

INTRODUCTION

Prediction of pharmacokinetic (PK) properties is crucial for drug discovery and development. Machine-learning (ML) models, which use statistical pattern recognition to learn correlations between input features (such as chemical structures) and target variables (such as PK parameters), are being increasingly used for this purpose. To embed ML models for PK prediction into workflows and to guide future development, a solid understanding of their applicability, advantages, limitations, and synergies with other approaches is necessary.

AREAS COVERED

This narrative review discusses the design and application of ML models to predict PK parameters of small molecules, especially in light of established approaches including in vitro-in vivo extrapolation (IVIVE) and physiologically based pharmacokinetic (PBPK) models. The authors illustrate scenarios in which the three approaches are used and emphasize how they enhance and complement each other. In particular, they highlight achievements, the state of the art and potentials of applying machine learning for PK prediction through a comphrehensive literature review.

EXPERT OPINION

ML models, when carefully crafted, regularly updated, and appropriately used, empower users to prioritize molecules with favorable PK properties. Informed practitioners can leverage these models to improve the efficiency of drug discovery and development process.

The Role of Genetics in Advancing Cardiometabolic Drug Development

PURPOSE OF REVIEW

The objective of this review is to explore the role of genetics in cardiometabolic drug development. The declining costs of sequencing and the availability of large-scale genomic data have deepened our understanding of cardiometabolic diseases, revolutionizing drug discovery and development methodologies. We highlight four key areas in which genetics is empowering drug development for cardiometabolic disease: (1) identifying drug candidates, (2) anticipating drug target failures, (3) silencing and editing genes, and (4) enriching clinical trials.

RECENT FINDINGS

Identifying novel drug targets through genetic discovery studies and the use of genetic variants as indicators of potential drug efficacy and safety have become critical components of cardiometabolic drug discovery. We highlight the successes of genetically-informed therapeutic strategies, such as PCSK9 and ANGPTL3 inhibitors in lipid lowering and the emerging role of polygenic risk scores in improving the efficiency of clinical trials. Additionally, we explore the potential of gene silencing and editing technologies, such as antisense oligonucleotides and small interfering RNA, showcasing their promise in addressing diseases refractory to conventional treatments. In this review, we highlight four use cases that demonstrate the vital role of genetics in cardiometabolic drug development: (1) identifying drug candidates, (2) anticipating drug target failures, (3) silencing and editing genes, and (4) enriching clinical trials. Through these advances, genetics has paved the way to increased efficiency of drug development as well as the discovery of more personalized and effective treatments for cardiometabolic disease.

Early, precise, and safe clinical evaluation of the pharmacodynamic effects of novel agents in the intact human tumor microenvironment

Introduction: Drug development is systemically inefficient. Research and development costs for novel therapeutics average hundreds of millions to billions of dollars, with the overall likelihood of approval estimated to be as low as 6.7% for oncology drugs. Over half of these failures are due to a lack of drug efficacy. This pervasive and repeated low rate of success exemplifies how preclinical models fail to adequately replicate the complexity and heterogeneity of human cancer. Therefore, new methods of evaluation, early in the development trajectory, are essential both to rule-in and rule-out novel agents with more rigor and speed, but also to spare clinical trial patients from the potentially toxic sequelae (high risk) of testing investigational agents that have a low likelihood of producing a response (low benefit). Methods: The clinical in vivo oncology (CIVO®) platform was designed to change this drug development paradigm. CIVO precisely delivers microdose quantities of up to 8 drugs or combinations directly into patient tumors 4-96 h prior to planned surgical resection. Resected tissue is then analyzed for responses at each site of intratumoral drug exposure. Results: To date, CIVO has been used safely in 6 clinical trials, including 68 subjects, with 5 investigational and 17 approved agents. Resected tissues were analyzed initially using immunohistochemistry and in situ hybridization assays (115 biomarkers). As technology advanced, the platform was paired with spatial biology analysis platforms, to successfully track anti-neoplastic and immune-modulating activity of the injected agents in the intact tumor microenvironment. Discussion: Herein we provide a report of the use of CIVO technology in patients, a depiction of the robust analysis methods enabled by this platform, and a description of the operational and regulatory mechanisms used to deploy this approach in synergistic partnership with pharmaceutical partners. We further detail how use of the CIVO platform is a clinically safe and scientifically precise alternative or complement to preclinical efficacy modeling, with outputs that inform, streamline, and de-risk drug development.

Is the price right? Paying for value today to get more value tomorrow

BACKGROUND

Contemporary debates about drug pricing feature several widely held misconceptions, including the relationship between incentives and innovation, the proportion of total healthcare spending on pharmaceuticals, and whether the economic evaluation of a medicine can be influenced by things other than clinical efficacy.

MAIN BODY

All citizens should have access to timely, equitable, and cost-effective care covered by public funds, private insurance, or a combination of both. Better managing the collective burden of diseases borne by today's and future generations depends in part on developing better technologies, including better medicines. As in any innovative industry, the expectation of adequate financial returns incentivizes innovators and their investors to develop new medicines. Estimating expected returns requires that they forecast revenues, based on the future price trajectory and volume of use over time. How market participants decide what price to set or accept can be complicated, and some observers and stakeholders want to confirm whether the net prices society pays for novel medicines, whether as a reward for past innovation or an incentive for future innovation, are commensurate with those medicines' incremental value. But we must also ask "value to whom?"; medicines not only bring immediate clinical benefits to patients treated today, but also can provide a broad spectrum of short- and long-term benefits to patients, their families, and society. Spending across all facets of healthcare has grown over the last 25 years, but both inpatient and outpatient spending has outpaced drug spending growth even as our drug armamentarium is constantly improving with safer and more effective medicines. In large part, this is because, unlike hospitals, drugs typically go generic, thus making room in our budgets for new and better ones, even as they often keep patients out of hospitals, driving further savings.

CONCLUSION

A thorough evaluation of drug spending and value can help to promote a better allocation of healthcare resources for both the healthy and the sick, both of whom must pay for healthcare. Taking a holistic approach to assessing drug value makes it clear that a branded drug's value to a patient is often only a small fraction of the drug's total value to society. Societal value merits consideration when determining whether and how to make a medicine affordable and accessible to patients: a drug that is worth its price to society should not be rendered inaccessible to ill patients by imposing high out-of-pocket costs or restricting coverage based on narrow health technology assessments (HTAs). Furthermore, recognizing the total societal cost of un- or undertreated conditions is crucial to gaining a thorough understanding of what guides the biomedical innovation ecosystem to create value for society. It would be unwise to discourage the development of new solutions without first appreciating the cost of leaving the problems unsolved.

How Labor Costs Affect Innovation Output in Pharmaceutical Companies: Evidence from China

Existing literature generally suggests that rising labor costs lead to the substitution of capital for labor, prompting firms to save on labor costs through technological upgrades. However, as a typical human capital-intensive industry, the pharmaceutical sector finds it challenging to replace labor with capital through the introduction of advanced equipment. Therefore, compared to other industries, the pharmaceutical sector faces greater adverse impacts. Research on how pharmaceutical R&D behavior is influenced by labor costs is scarce. This paper analyzes the triple effects of rising labor costs on corporate innovation from the perspectives of human capital, physical capital, and financial capital. Based on empirical research using data from Chinese listed companies, we found that an increase in labor costs promotes innovation output in the pharmaceutical sector, but this effect is more pronounced in other sectors. Financing constraints play a negative role on corporate innovation in the pharmaceutical sector, while it is not significant in the other sectors. Factor substitution play a positive effect on corporate innovation in the other sectors, which is invalid in the pharmaceutical sector. This research contributes to a deeper understanding of the unique mechanisms by which labor costs impact innovation activities in the pharmaceutical industry.

Artificial Intelligence/Machine Learning-Driven Small Molecule Repurposing via Off-Target Prediction and Transcriptomics

The process of discovering small molecule drugs involves screening numerous compounds and optimizing the most promising ones, both in vitro and in vivo. However, approximately 90% of these optimized candidates fail during trials due to unexpected toxicity or insufficient efficacy. Current concepts with respect to drug-protein interactions suggest that each small molecule interacts with an average of 6-11 targets. This implies that approved drugs and even discontinued compounds could be repurposed by leveraging their interactions with unintended targets. Therefore, we developed a computational repurposing framework for small molecules, which combines artificial intelligence/machine learning (AI/ML)-based and chemical similarity-based target prediction methods with cross-species transcriptomics information. This repurposing methodology incorporates eight distinct target prediction methods, including three machine learning methods. By using multiple orthogonal methods for a "dataset" composed of 2766 FDA-approved drugs targeting multiple therapeutic target classes, we identified 27,371 off-target interactions involving 2013 protein targets (i.e., an average of around 10 interactions per drug). Relative to the drugs in the dataset, we identified 150,620 structurally similar compounds. The highest number of predicted interactions were for drugs targeting G protein-coupled receptors (GPCRs), enzymes, and kinases with 10,648, 4081, and 3678 interactions, respectively. Notably, 17,283 (63%) of the off-target interactions have been confirmed in vitro. Approximately 4000 interactions had an IC50 of <100 nM for 1105 FDA-approved drugs and 1661 interactions had an IC50 of <10 nM for 696 FDA-approved drugs. Together, the confirmation of numerous predicted interactions and the exploration of tissue-specific expression patterns in human and animal tissues offer insights into potential drug repurposing for new therapeutic applications.

Is Target-Based Drug Discovery Efficient? Discovery and "Off-Target" Mechanisms of All Drugs

Target-based drug discovery is the dominant paradigm of drug discovery; however, a comprehensive evaluation of its real-world efficiency is lacking. Here, a manual systematic review of about 32000 articles and patents dating back to 150 years ago demonstrates its apparent inefficiency. Analyzing the origins of all approved drugs reveals that, despite several decades of dominance, only 9.4% of small-molecule drugs have been discovered through "target-based" assays. Moreover, the therapeutic effects of even this minimal share cannot be solely attributed and reduced to their purported targets, as they depend on numerous off-target mechanisms unconsciously incorporated by phenotypic observations. The data suggest that reductionist target-based drug discovery may be a cause of the productivity crisis in drug discovery. An evidence-based approach to enhance efficiency seems to be prioritizing, in selecting and optimizing molecules, higher-level phenotypic observations that are closer to the sought-after therapeutic effects using tools like artificial intelligence and machine learning.