The endless frontier? The recent increase of R&D productivity in pharmaceuticals

Metabolomics integrated genomics approach: Understanding multidrug resistance phenotype in MCF-7 breast cancer cells exposed to doxorubicin and ABCA1/EGFR/PI3k/PTEN crosstalk

Resistance of cancer cells, especially breast cancer, to therapeutic medicines represents a major clinical obstacle that impedes the stages of treatment. Carcinoma cells that acquire resistance to therapeutic drugs can reprogram their own metabolic processes as a way to overcome the effectiveness of treatment and continue their reproduction processes. Despite the recent developments in medical research in the field of drug resistance, which showed some explanations for this phenomenon, the real explanation, along with the ability to precisely predict the possibility of its occurrence in breast cancer cells, still necessitates a deep consideration of the dynamics of the tumor's response to treatment. For this purpose the current study, combined both in vitro metabolomics and in vivo genomics analysis as the most advanced omics technologies that can provide a potential en route for inventing novel strategies to perform prospective, prognostic and diagnostic biomarkers for drug resistance phenomena in mammary cancer. Doxorubicin is the currently available breast cancer chemotherapeutic medication nevertheless; it was demonstrated to cause drug resistance, which impairs patient survival and prognosis by prompting proliferation, cell cycle progression, and preventing apoptosis, interactions between signaling pathways triggered drug resistance. In this research, in vitro metabolomics analysis based on GC-MS coupled with multivariable analysis was performed on MCF-7 and DOX resistant cell lines; MCF-7/adr cultured cells in addition to, further in vivo confirmation via inducing mammary cancer in rats via two doses of 7,12-dimethylbenz(a) anthracene (DMBA) (50 mg/kg and 25 mg/kg) proceeded by doxorubicin (5 mg/kg) treatment for one month. The metabolomics in vitro results pointed out that mannitol, myoinositol, glycine, α-linolenic acid, oleic acid and stearic acid have AUC values: 0.14, 0.5, 0.7, 0.1, 0.02, -0.02 (1, 1) respectively. Glycine and myoinositol metabolites provided the best discriminative power in the wild and resistance MCF-7 phenotypes. Meanwhile, in vivo results revealed a significant crosstalk between the alternation in oxidative stress biomarkers as well as Arginase II tumor biomarker and the molecular assessment of ABCA1 and P53 gene expression that displayed a marked reduction in addition to, the obvious elevation in resistance and apoptotic biomarkers EGFR/PI3k/AKT/PTEN signaling pathway upon DMBA administration. Data revealed a significant alternation in signaling pathways related to resistance upon doxorubicin administration that affect lipid metabolism in breast cancer. In conclusion, Metabolomics integrated genomics analysis may be promising in understanding multidrug resistance phenotype in MCF-7 breast cancer cells exposed to doxorubicin through modulating ABCA1/EGFR/P53/PI3k/PTEN signaling pathway thus metabolic biomarkers in addition to molecular biomarkers elucidate the challenges fronting profitable therapy of mammary cancer and an pioneering approaches that metabolomics compromises to improve recognizing drug resistance in breast carcinoma.

Mapping the Clinical Development Trajectory of Cell and Gene Therapy Products

While cell and gene therapies (CGTs) have emerged as promising modalities to treat conditions with limited therapeutic options, their unconventional development is fraught with uncertainty, rendering them high-risk assets for many pharmaceutical companies. Here, we assess the clinical development trajectories of CGT products by estimating probabilities of successful clinical trial phase transitions and the likelihood of achieving regulatory approval. We included all CGT products entering clinical development from 1993 to 2023 and intended for marketing in the United States, Europe, Japan, Canada, and Switzerland. Associations between product success and characteristics were investigated. In sub-analyses, we examined the clinical trajectories of two promising product types, chimeric antigen receptor T (CAR T) cell therapies and adeno-associated viral (AAV) vector-based gene therapies. We identified 995 CGT products corresponding to 1,961 development programs. A total of 44 CGTs secured at least one regulatory approval, corresponding to an overall likelihood of approval of 5.3% (95% CI 4.0-6.9). Development programs with an orphan designation had a higher likelihood of approval than those without (9.4%, 95% CI 6.6-13.3 vs. 3.2%, 95% CI 2.0-4.9), while programs for oncology indications had a lower likelihood of approval compared to those for non-oncology indications (3.2%, 95% CI 1.6-5.1 vs. 8.0%, 95% CI 5.7-11.1). CAR T cells and AAV gene therapies had a similar overall likelihood of approval of 13.6% (95% CI 7.3, 23.9) and 13.6% (95% CI 6.4, 26.7), respectively. In conclusion, CGT products have a low overall likelihood of approval with variability based on orphan status, therapeutic area, and product type.

Application of Machine Learning and Mechanistic Modeling to Predict Intravenous Pharmacokinetic Profiles in Humans

Accurate prediction of new compounds' pharmacokinetic (PK) profile in humans is crucial for drug discovery. Traditional methods, including allometric scaling and mechanistic modeling, rely on parameters from in vitro or in vivo testing, which are labor-intensive and involve ethical concerns. This study leverages machine learning (ML) to overcome these limitations by developing data-driven models. We compiled a large data set of small molecules' physicochemical and PK properties from public sources and digitized human plasma concentration-time profiles for approximately 800 compounds from the literature. We introduced a hybrid modeling framework that combines ML with physiologically based pharmacokinetic modeling and a hierarchical ML framework that employs two steps of learning to directly estimate PK profiles. Tested on 106 drugs, these frameworks demonstrated prediction accuracies within a 2-fold and 5-fold error for 40-60% and 80%-90% of compounds, respectively, in both AUC and Cmax. Proposed approaches could enhance early molecular screening and design, advancing drug discovery capabilities.

Drug target assessments: classifying target modulation and associated health effects using multi-level BERT-based classification models

Motivation

Drug target selection determines the success of the drug development pipeline. Therefore, novel drug targets need to be assessed for their therapeutic benefits/risks at the earliest stage possible. Where manual risk/benefit analyses are often user-biased and time-consuming, Large Language Models can offer a systematic and efficient approach to curating and analysing literature. Currently, publicly available Large Language Models are lacking for this task, while public platforms for target assessments are limited to co-occurrences.

Results

BERT-models for multi-level classification of drug target-health effect relationships described in PubMed were developed. Relationships were classified based on (i) causality; (ii) direction of target modulation; (iii) direction of the associated health effect. The models showed competitive performances with F1 scores between 0.86 and 0.92 and their applicability was demonstrated using ADAM33 and OSM as case study. The developed classification pipeline is the first to allow detailed classification of drug target-health effect relationships. The models provide mechanistic insight into how target modulation affects health and disease, both from an efficacy and safety perspective. The models, deployed on the whole of PubMed and available through the TargetTri platform, are expected to offer a significant advancement in artificial intelligence-assisted target identification and evaluation.

Availability and implementation

https://www.targettri.com.

Reforming the innovation system to deliver affordable medicines: a conceptual framework of pharmaceutical innovation as a complex adaptive system (forest) and theory of change

Background

The current mainstream pharmaceutical innovation system (PIS) is driven by the market-based logic of charging the highest prices societies will bear. Outcomes include unaffordable medicines, restricted access and pressure on health budgets. How can the innovation system change to deliver fairly-priced medicines?

Methods

We inductively developed a novel conceptual framework of the PIS as a complex adaptive system (CAS) analogous to a forest. We constructed a database of 140 pharmaceutical innovation initiatives that sought to address global public interest objectives such as fair pricing or missing innovation. We found a critical mass of initiatives clustered around four areas: pandemic preparedness, neglected diseases, rare diseases and antibiotics, which we conceptualised as niches within the ecosystem. We reviewed the literature on how each niche had emerged and evolved, conducted interviews, and organised workshops with experts on each niche. Finally, we identified from the literature an initial list of 'levers' of change in the PIS, supplemented them with additional levers found in each niche, then compared across niches.

Results

We found that actors created niches in the broader system by purposefully problematising an issue, then pulling on one or more of three levers: mobilising new resources, changing the roles of or creating new actors, and/or changing societal norms or legal rules. A wide range of actors - including governments, funders, R&D practitioners, or civil society groups - could pull these levers, and the order in which they were pulled was not fixed, consistent with a CAS.

Conclusions

Parts of the vast pharmaceutical innovation system have changed to deliver more affordable medicines by design. Such change has occurred largely within specialised niches, responding to evolving societal norms about the purpose of pharmaceutical innovation. Actors can achieve larger-scale change by further expanding and/or solidifying these niches through changes to resources, actor roles, norms and rules.

Opportunities of topical drug products in a changing dermatological landscape

Despite the prevalence and the impact on quality of life of dermatological indications, drug products to treat such conditions have rarely been blockbusters. The prevailing perception of a limited commercial potential of dermatological drug products has restricted innovation and encouraged a more conservative development approach. For example, the focus was on repurposing/reformulation of existing active pharmaceutical ingredients (APIs) specifically for the topical delivery route. However, the situation is quite different today catalyzed in part by the blockbuster success of Dupixent (dupilumab), the first monoclonal antibody treatment for atopic dermatitis which has been approved by the US Food and Drug Administration (US FDA) in 2017. Dupixent's success not only encouraged the development of other biologics but also inspired the (re-)development of new dermal drug products that can reap the many benefits of topical administration. We have also witnessed a shift toward outsourcing development efforts (and associated risks) towards small- to mid-size pharmaceutical companies which often require support of contract research and development/manufacturing organizations (CRO and CDMO). Such trends also emphasize the need of greater expertise in topical formulation design, as well as associated commercial and regulatory considerations. Today, we believe that topical drug products remain not only an essential but also commercially viable class of dermatological therapeutics. In this opinion article, we will address the challenges as well as opportunities of coherent development strategies in the current market environment, formulation innovations of topical drug products and technological advances to facilitate rational topical drug formulation development.

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.

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.

Algorithmic benchmark modulation: A novel method to develop success rates for clinical studies

BACKGROUND

High-quality decision-making in the pharmaceutical industry requires accurate assessments of the Probability of Technical Success of clinical trials. Failure to do so will lead to lost opportunities for both patients and investors. Pharmaceutical companies employ different methodologies to determine Probability of Technical Success values. Some companies use power and assurance calculations; others prefer to use industry benchmarks with or without the overlay of subjective modulations. At AstraZeneca, both assurance calculations and industry benchmarks are used, and both methods are combined with modulations.

METHODS

AstraZeneca has recently implemented a simple algorithm that allows for modulation of a Probability of Technical Success value. The algorithm is based on a set of multiple-choice questions. These questions cover a comprehensive set of issues that have historically been considered by AstraZeneca when subjective modulations to Probability of Technical Success values were made but do so in a much more structured way.

RESULTS

A set of 57 phase 3 Probability of Technical Success assessments suggests that AstraZeneca's historical estimation of Probability of Technical Success has been reasonably accurate. A good correlation between the subjective modulation and the modulation algorithm was found. This latter observation, combined with the finding that historically AstraZeneca has been reasonably accurate in its estimation of Probability of Technical Success, gives confidence in the validity of the novel method.

DISCUSSION

Although it is too early to demonstrate whether the method has improved the accuracy of company's Probability of Technical Success assessments, we present our data and analysis here in the hope that it may assist the pharmaceutical industry in addressing this key challenge. This new methodology, developed for pivotal studies, enables AstraZeneca to develop more consistent Probability of Technical Success assessments with less effort and can be used to adjust benchmarks as well as assurance calculations.

CONCLUSION

The Probability of Technical Success modulation algorithm addresses several concerns generally associated with assurance calculations or benchmark without modulation: selection biases, situations where little relevant prior data are available and the difficulty to model many factors affecting study outcomes. As opposed to using industry benchmarks, the Probability of Technical Success modulation algorithm allows to accommodate project-specific considerations.

Viruses traverse the human proteome through peptide interfaces that can be biomimetically leveraged for drug discovery

We present a drug design strategy based on structural knowledge of protein-protein interfaces selected through virus-host coevolution and translated into highly potential small molecules. This approach is grounded on Vinland, the most comprehensive atlas of virus-human protein-protein interactions with annotation of interacting domains. From this inspiration, we identified small viral protein domains responsible for interaction with human proteins. These peptides form a library of new chemical entities used to screen for replication modulators of several pathogens. As a proof of concept, a peptide from a KSHV protein, identified as an inhibitor of influenza virus replication, was translated into a small molecule series with low nanomolar antiviral activity. By targeting the NEET proteins, these molecules turn out to be of therapeutic interest in a nonalcoholic steatohepatitis mouse model with kidney lesions. This study provides a biomimetic framework to design original chemistries targeting cellular proteins, with indications going far beyond infectious diseases.

Towards better antivenoms: navigating the road to new types of snakebite envenoming therapies

Snakebite envenoming is a significant global health challenge, and for over a century, traditional plasma-derived antivenoms from hyperimmunized animals have been the primary treatment against this infliction. However, these antivenoms have several inherent limitations, including the risk of causing adverse reactions when administered to patients, batch-to-batch variation, and high production costs. To address these issues and improve treatment outcomes, the development of new types of antivenoms is crucial. During this development, key aspects such as improved clinical efficacy, enhanced safety profiles, and greater affordability should be in focus. To achieve these goals, modern biotechnological methods can be applied to the discovery and development of therapeutic agents that can neutralize medically important toxins from multiple snake species. This review highlights some of these agents, including monoclonal antibodies, nanobodies, and selected small molecules, that can achieve broad toxin neutralization, have favorable safety profiles, and can be produced on a large scale with standardized manufacturing processes. Considering the inherent strengths and limitations related to the pharmacokinetics of these different agents, a combination of them might be beneficial in the development of new types of antivenom products with improved therapeutic properties. While the implementation of new therapies requires time, it is foreseeable that the application of biotechnological advancements represents a promising trajectory toward the development of improved therapies for snakebite envenoming. As research and development continue to advance, these new products could emerge as the mainstay treatment in the future.

Patient-derived tumor models in cancer research: Evaluation of the oncostatic effects of melatonin

The development of new anticancer therapies tends to be very slow. Although their impact on potential candidates is confirmed in preclinical studies, ∼95 % of these new therapies are not approved when tested in clinical trials. One of the main reasons for this is the lack of accurate preclinical models. In this context, there are different patient-derived models, which have emerged as a powerful oncological tool: patient-derived xenografts (PDXs), patient-derived organoids (PDOs), and patient-derived cells (PDCs). Although all these models are widely applied, PDXs, which are created by engraftment of patient tumor tissues into mice, is considered more reliable. In fundamental research, the PDX model is used to evaluate drug-sensitive markers and, in clinical practice, to select a personalized therapeutic strategy. Melatonin is of particular importance in the development of innovative cancer treatments due to its oncostatic impact and lack of adverse effects. However, the literature regarding the oncostatic effect of melatonin in patient-derived tumor models is scant. This review aims to describe the important role of patient-derived models in the development of anticancer treatments, focusing, in particular, on PDX models, as well as their use in cancer research. This review also summarizes the existing literature on the anti-tumoral effect of melatonin in patient-derived models in order to propose future anti-neoplastic clinical 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.

Doing science together: Gaining momentum from long-term explorative university-industry research programs

'Doing science together' collaborations are a more intense form of university-industry interactions and are characterized by a mutual involvement and active participation of academic and company scientists in scientific research. Here, we examine the successful approach that AstraZeneca and its internationally renowned academic partners, Karolinska Institutet and Uppsala University, implemented to fully unlock the potential of all parties in long-term, explorative, truly collaborative research programs. The underlying premises of these successful research programs are three collaborative governance mechanisms (3MCs) that are required that leverage the strengths of each organization: mutual collaboration; mutually beneficial science; and a mutual governance model with senior management involvement.

Factors Affecting Success of New Drug Clinical Trials

Clinical trials are an essential process in the development of new drugs. In spite of time-consuming processes and high costs, the overall success rate of clinical trials is only 7.9%, which is a high risk for biopharmaceutical companies. However, despite these huge risks, research on finding factors affecting clinical trials to overcome and manage to risks has been insufficient. Considering these characteristics of the pharmaceutical industry, this study investigated the factors affecting the success of sponsor-initiated clinical trials. The success factors investigated were categorized into four factors: quality of clinical trials, speed of clinical trials, relationship type, and communication. Logistic regression was performed to measure each factor by analyzing 24,295 cases of Phase 1 to 4 trials from ClinicalTrials.gov. Because of the analysis, the factors affecting the success of the clinical trials were varied according to each clinical phase and the drug types: New Molecular Entity (NME)/Biologics, and the success ratio in the quality variable affected the overall clinical trial phases. Additionally, the experience, speed, relationship type, and communication variables were also found to be statistically significant for the success of each phase and drug type.

Embedding Dynamics in Intrinsic Physicochemical Profiles of Market-Stage Antibody-Based Biotherapeutics

Adequate stability, manufacturability, and safety are crucial to bringing an antibody-based biotherapeutic to the market. Following the concept of holistic in silico developability, we introduce a physicochemical description of 91 market-stage antibody-based biotherapeutics based on orthogonal molecular properties of variable regions (Fvs) embedded in different simulation environments, mimicking conditions experienced by antibodies during manufacturing, formulation, and in vivo. In this work, the evaluation of molecular properties includes conformational flexibility of the Fvs using molecular dynamics (MD) simulations. The comparison between static homology models and simulations shows that MD significantly affects certain molecular descriptors like surface molecular patches. Moreover, the structural stability of a subset of Fv regions is linked to changes in their specific molecular interactions with ions in different experimental conditions. This is supported by the observation of differences in protein melting temperatures upon addition of NaCl. A DEvelopability Navigator In Silico (DENIS) is proposed to compare mAb candidates for their similarity with market-stage biotherapeutics in terms of physicochemical properties and conformational stability. Expanding on our previous developability guidelines (Ahmed et al. Proc. Natl. Acad. Sci. 2021, 118 (37), e2020577118), the hydrodynamic radius and the protein strand ratio are introduced as two additional descriptors that enable a more comprehensive in silico characterization of biotherapeutic drug candidates. Test cases show how this approach can facilitate identification and optimization of intrinsically developable lead candidates. DENIS represents an advanced computational tool to progress biotherapeutic drug candidates from discovery into early development by predicting drug properties in different aqueous environments.

Regulatory policy and pharmaceutical innovation in the United Kingdom after Brexit: Initial insights

Brexit was presented as an opportunity to promote innovation by breaking free from the European Union regulatory framework. Since the beginning of 2021 the Medicines and Healthcare products Regulatory Agency (MHRA) has operated as the independent regulatory agency for the United Kingdom. The MHRA's regulatory activity in 2021 was analyzed and compared to that of other international regulatory bodies. The MHRA remained reliant on EU regulatory decision-making for novel medicines and there were significant regulatory delays for a small number of novel medicines in the UK, the reasons being so far unclear. In addition, the MHRA introduced innovation initiatives, which show early promise for quicker authorization of innovative medicines for cancer and other areas of unmet need. Longer-term observation and analysis is needed to show the full impact of post-Brexit pharmaceutical regulatory policy.

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.

An overview of Fintech applications to solve the puzzle of health care funding: state-of-the-art in medical crowdfunding

Crowdfunding is emerging as an alternative form of funding for medical purposes, with capital being raised directly from a broader and more diverse audience of investors. In this paper, we have systematically researched and reviewed the literature on medical crowdfunding to determine how crowdfunding connects with the health care industry. The health care industry has been struggling to develop sustainable research and business models for economic systems and investors alike, especially in pharmaceuticals. The research results have revealed a wealth of evidence concerning the way crowdfunding is applied in real life. Patients and caregivers utilize web platform-based campaigns all over the world to fund their medical expenses, generally on a spot basis, using donation-based or even reward-based schemes, regardless of the health care system archetype (public, private insurance-based or hybrid). Academics have also focused on funding campaigns and the predictors of success (which range from social behaviour and environment to the basic demographics of the campaigners and their diseases) and on social and regulatory concerns, including heightened social inequality and stigma. While equity crowdfunding is disrupting the way many ventures/businesses seek capital in the market, our research indicates that there are no relevant or consistent data on the practice of medical equity crowdfunding in health care, apart from a few anecdotal cases.

Industrializing AI-powered drug discovery: lessons learned from the Patrimony computing platform

INTRODUCTION

As a mid-size international pharmaceutical company, we initiated four years ago the launch of a dedicated high-throughput computing platform supporting drug discovery. The platform named "Patrimony" was built-up on the initial predicate to capitalize on our proprietary data while leveraging public data sources in order to foster a Computational Precision Medicine approach with the power of Artificial Intelligence.

AREAS COVERED

Specifically, Patrimony is designed to identify novel therapeutic target candidates. With several successful use cases in Immuno-inflammatory diseases, and current ongoing extension to applications to Oncology and Neurology, we document how this industrial computational platform has had a transformational impact on our R&D, making it more competitive, as well time and cost effective through a model-based educated selection of therapeutic targets and drug candidates.

EXPERT OPINION

We report our achievements, but also our challenges in implementing data access and governance processes, building-up hardware and user interfaces, and acculturing scientists to use predictive models to inform decisions.

Operationalising AI governance through ethics-based auditing: an industry case study

Ethics-based auditing (EBA) is a structured process whereby an entity's past or present behaviour is assessed for consistency with moral principles or norms. Recently, EBA has attracted much attention as a governance mechanism that may help to bridge the gap between principles and practice in AI ethics. However, important aspects of EBA-such as the feasibility and effectiveness of different auditing procedures-have yet to be substantiated by empirical research. In this article, we address this knowledge gap by providing insights from a longitudinal industry case study. Over 12 months, we observed and analysed the internal activities of AstraZeneca, a biopharmaceutical company, as it prepared for and underwent an ethics-based AI audit. While previous literature concerning EBA has focussed on proposing or analysing evaluation metrics or visualisation techniques, our findings suggest that the main difficulties large multinational organisations face when conducting EBA mirror classical governance challenges. These include ensuring harmonised standards across decentralised organisations, demarcating the scope of the audit, driving internal communication and change management, and measuring actual outcomes. The case study presented in this article contributes to the existing literature by providing a detailed description of the organisational context in which EBA procedures must be integrated to be feasible and effective.

In Vitro to In Vivo Extrapolation Linked to Physiologically Based Pharmacokinetic Models for Assessing the Brain Drug Disposition

Drug development for the central nervous system (CNS) is a complex endeavour with low success rates, as the structural complexity of the brain and specifically the blood-brain barrier (BBB) poses tremendous challenges. Several in vitro brain systems have been evaluated, but the ultimate use of these data in terms of translation to human brain concentration profiles remains to be fully developed. Thus, linking up in vitro-to-in vivo extrapolation (IVIVE) strategies to physiologically based pharmacokinetic (PBPK) models of brain is a useful effort that allows better prediction of drug concentrations in CNS components. Such models may overcome some known aspects of inter-species differences in CNS drug disposition. Required physiological (i.e. systems) parameters in the model are derived from quantitative values in each organ. However, due to the inability to directly measure brain concentrations in humans, compound-specific (drug) parameters are often obtained from in silico or in vitro studies. Such data are translated through IVIVE which could be also applied to preclinical in vivo observations. In such exercises, the limitations of the assays and inter-species differences should be adequately understood in order to verify these predictions with the observed concentration data. This report summarizes the state of IVIVE-PBPK-linked models and discusses shortcomings and areas of further research for better prediction of CNS drug disposition.

How much do the public sector and the private sector contribute to biopharmaceutical R&D?

When examining the prices of new medicines, the question of how much the private and public sectors have contributed to their R&D is often raised. Contributions can be assessed in terms of the investment, authorship of publications, marketing authorizations and intellectual property rights associated with biopharmaceutical R&D. This review of the empirical evidence underlines the complementary and interwoven nature of the private and public sectors in supporting biopharmaceutical R&D. Both sectors invest in and contribute to biopharmaceutical R&D, with the public sector predominantly focusing on basic research and the private sector mainly targeting medicine discovery and development. Public-sector investment generates additional private-sector investment.

To blind or not to blind first in human and exploratory clinical trials: Acceleration of development vs. risk of bias

An IQ consortium working group (WG) conducted a survey across multiple biopharmaceutical companies to gain information about the level of blinding commonly utilized for early clinical development trials. The main objectives were: (1) to understand blinding practices between healthy volunteer (HV) and early explorative patient trials in all therapeutic areas except oncology where early clinical trials are commonly open‐label; (2) to understand the rationale for blinding/unblinding practices; (3) to understand the groups and personnel involved in unblinding; and (4) strategic considerations around blinding/unblinding options in early clinical development trials—risk of bias vs. potential for acceleration. A survey containing 31 main questions with additional sub‐clarifying questions was conducted. Sixteen large and mid‐size pharmaceutical companies responded. Responses were aligned across functions within each participating company. Additional information was gathered at an American Association of Pharmaceutical Scientists (AAPS) webinar with polling options to roughly 550 registered attendees to evaluate the reason for the unblinding decisions. The results revealed divergence across companies in the blinding approaches most commonly applied but with some study types, there were clearly favored options. Based on these results, the WG developed strategic considerations for first‐in‐human HV trials and nonpivotal explorative trials in patients. This paper should facilitate discussions among various clinical development functions, such as Clinical Pharmacology, Statistics, Clinical, Bioanalytics, and Regulatory Functions. Such discussions on study design and operations are warranted to allow implementation of more flexible blinding approaches to accelerate data driven decisions in drug development and allow earlier access of patients to needful medicines.

R&D Costs of New Medicines: A Landscape Analysis

Over the years, questions have been raised over R&D costs of new medicines. The aim of this study is to conduct a landscape review of the (drivers of) R&D costs of a new medicine derived from the peer-reviewed and grey literature. Included studies have drawn data either from confidential company surveys or from publicly available company financial statements, in addition to accessing the literature and medicine information databases. Although there were differences in methodology, parameter values, samples and time periods between studies, estimates of R&D costs per new medicine (accounting for the cost of failures) ranged from US$944m to US$2,826m (adjusted to 2019 prices). The evidence also suggested that R&D costs per new medicine have increased over time. A few studies have broken down total costs and showed that clinical development accounts for 50-58% of R&D costs per new medicine. R&D costs were influenced by costs of discovery and pre-clinical development, costs of clinical development, cost of capital, company and product profile. Finally, cost estimates are likely to be dynamic as the biopharmaceutical industry and the broader environment continue to evolve.

How Much Does It Cost to Research and Develop a New Drug? A Systematic Review and Assessment

BACKGROUND

Debate over the viability of the current commercial research and development (R&D) model is ongoing. A controversial theme is the cost of bringing a new molecular entity (NME) to market.

OBJECTIVE

Our aim was to evaluate the range and suitability of published R&D cost estimates as to the degree to which they represent the actual costs of industry.

METHODS

We provided a systematic literature review based on articles found in the Pubmed, Embase and EconLit electronic databases, and in a previously published review. Articles published before March 2020 that estimated the total R&D costs were included (22 articles with 45 unique cost estimates). We included only literature in which the methods used to collect the information and to estimate the R&D costs were clearly described; therefore, three reports were excluded. We extracted average pre-launch R&D costs per NME and converted the values to 2019 US dollars (US$) using the gross domestic product (GDP) price deflator. We appraised the suitability of the R&D estimated costs by using a scoring system that captures three domains: (1) how success rates and development time used for cost estimation were obtained; (2) whether the study considered potential sources contributing to the variation in R&D costs; and (3) what the components of the cost estimation were.

RESULTS

Estimates of total average capitalized pre-launch R&D costs varied widely, ranging from $161 million to $4.54 billion (2019 US$). Therapeutic area-specific estimates were highest for anticancer drugs (between $944 million and $4.54 billion). Our analysis identified a trend of increasing R&D costs per NME over time but did not reveal a relation between cost estimates and study ranking when the suitability scores were assessed. We found no evidence of an increase in suitability scores over time.

CONCLUSION

There is no universally correct answer regarding how much it costs, on average, to research and develop an NME. Future studies should explicitly address previously neglected variables, which likely explain some variability in estimates, and consider the trade-off between the transparency and public accessibility of data and their specificity. Use of our proposed suitability scoring system may assist in addressing such issues.

Modelling Human Physiology on-Chip: Historical Perspectives and Future Directions

For centuries, animal experiments have contributed much to our understanding of mechanisms of human disease, but their value in predicting the effectiveness of drug treatments in the clinic has remained controversial. Animal models, including genetically modified ones and experimentally induced pathologies, often do not accurately reflect disease in humans, and therefore do not predict with sufficient certainty what will happen in humans. Organ-on-chip (OOC) technology and bioengineered tissues have emerged as promising alternatives to traditional animal testing for a wide range of applications in biological defence, drug discovery and development, and precision medicine, offering a potential alternative. Recent technological breakthroughs in stem cell and organoid biology, OOC technology, and 3D bioprinting have all contributed to a tremendous progress in our ability to design, assemble and manufacture living organ biomimetic systems that more accurately reflect the structural and functional characteristics of human tissue in vitro, and enable improved predictions of human responses to drugs and environmental stimuli. Here, we provide a historical perspective on the evolution of the field of bioengineering, focusing on the most salient milestones that enabled control of internal and external cell microenvironment. We introduce the concepts of OOCs and Microphysiological systems (MPSs), review various chip designs and microfabrication methods used to construct OOCs, focusing on blood-brain barrier as an example, and discuss existing challenges and limitations. Finally, we provide an overview on emerging strategies for 3D bioprinting of MPSs and comment on the potential role of these devices in precision medicine.

Fueling the Pipeline via Innovations in Organic Synthesis

The paramount importance of synthetic organic chemistry in the pharmaceutical industry arises from the necessity to physically prepare all designed molecules to obtain key data to feed the design-synthesis-data cycle, with the medicinal chemist at the center of this cycle. Synthesis specialists accelerate the cycle of medicinal chemistry innovation by rapidly identifying and executing impactful synthetic methods and strategies to accomplish project goals, addressing the synthetic accessibility bottleneck that often plagues discovery efforts. At AbbVie, Discovery Synthesis Groups (DSGs) such as Centralized Organic Synthesis (COS) have been deployed as embedded members of medicinal chemistry teams, filling the gap between discovery and process chemistry. COS chemists provide synthetic tools, scaffolds, and lead compounds to fuel the pipeline. Examples of project contributions from neuroscience, cystic fibrosis, and virology illustrate the impact of the DSG approach. In the first ten years of innovative science in pursuit of excellence in synthesis, several advanced drug candidates, including ABBV-2222 (galicaftor) for cystic fibrosis and foslevodopa/foscarbidopa for Parkinson's disease, have emerged with key contributions from COS.

Opinion on Maintaining In-House GLP Status for Toxicologic Pathology in Pharmaceutical and (Agro)Chemical Development

Many pharmaceutical companies have recently elected to stop maintaining good laboratory practices (GLP) status of their R&D sites. Similar discussions have also been engaged in the (agro)chemical industry. This opinion paper examines the pros and cons of maintaining facility GLP status for the purposes of performing the pathology interpretation or peer reviews of GLP studies internally. The toxicologic pathologist provides gross and histomorphologic evaluation and interpretation of nonclinical exploratory and regulatory studies during drug and (agro)chemical development. This assessment significantly contributes to human risk assessment by characterizing the toxicological profile and discussing the human relevance of the findings. The toxicologic pathologist is a key contributor to compound development decisions (advancement or termination) and in the development of de-risking strategies for backup compounds, thus playing a critical role in helping to reduce the late attrition of drugs and chemicals. Maintaining GLP compliance is often perceived as a costly and cumbersome process; a common and short-term strategy to reduce the costs is to outsource regulatory toxicity studies. However, there are significant advantages in maintaining the GLP status for toxicologic pathology activities in-house including the sustainable retention of internal pathology expertise that has maintained the necessary training needed to manage GLP studies. [Box: see text].

The fall of the innovation empire and its possible rise through open science

There is growing concern that the innovation system's ability to create wealth and attain social benefit is declining in effectiveness. This article explores the reasons for this decline and suggests a structure, the open science partnership, as one mechanism through which to slow down or reverse this decline. The article examines the empirical literature of the last century to document the decline. This literature suggests that the cost of research and innovation is increasing exponentially, that researcher productivity is declining, and, third, that these two phenomena have led to an overall flat or declining level of innovation productivity. The article then turns to three explanations for the decline - the growing complexity of science, a mismatch of incentives, and a balkanization of knowledge. Finally, the article explores the role that open science partnerships - public-private partnerships based on open access publications, open data and materials, and the avoidance of restrictive forms of intellectual property - can play in increasing the efficiency of the innovation system.

New Models for the Evaluation of Specialized Medicinal Products: Beyond Conventional Health Technology Assessment and Pricing

New specialized therapeutics coming to market, such as advanced therapy medicinal products (ATMPs) and orphan drugs, differ from traditional therapies in terms of how they are manufactured and administered, as well as the potentially transformative benefits they may provide. The current health technology assessment (HTA) process that has been used for traditional therapies, such as small molecule drugs and antibodies, does not work adequately for specialized therapeutics, with a key issue being the generation of sufficient evidence to adequately capture the full long-term benefits. The objectives of this article are to discuss why the current HTA process is inadequate for evaluating these new therapies, how evidence should be continuously generated and presented to regulators and payers to support their use, and to propose new approaches to pricing models. This will enable payers to have an affordable, risk-mitigated means of funding new therapies in a timely manner, thus guaranteeing patient access to new, potentially life-saving therapies, while providing manufacturers with a return on their investment. Without new approaches or adaptation of existing frameworks, certain ATMPs may not reach patients in some or all countries or be at risk of withdrawal from the market.

Use of artificial intelligence to enhance phenotypic drug discovery

Research and development (R&D) productivity across the pharmaceutical industry has received close scrutiny over the past two decades, especially taking into consideration reports of attrition rates and the colossal cost for drug development. The respective merits of the two main drug discovery approaches, phenotypic and target based, have divided opinion across the research community, because each hold different advantages for identifying novel molecular entities with a successful path to the market. Nevertheless, both have low translatability in the clinic. Artificial intelligence (AI) and adoption of machine learning (ML) tools offer the promise of revolutionising drug development, and overcoming obstacles in the drug discovery pipeline. Here, we assess the potential of target-driven and phenotypic-based approaches and offer a holistic description of the current state of the field, from both a scientific and industry perspective. With the emerging partnerships between AI/ML and pharma still in their relative infancy, we investigate the potential and current limitations with a particular focus on phenotypic drug discovery. Finally, we emphasise the value of public-private partnerships (PPPs) and cross-disciplinary collaborations to foster innovation and facilitate efficient drug discovery programmes.

The Role of 89Zr-Immuno-PET in Navigating and Derisking the Development of Biopharmaceuticals

The identification of molecular drivers of disease and the compelling rise of biotherapeutics have impacted clinical care but have also come with challenges. Such therapeutics include peptides, monoclonal antibodies, antibody fragments and nontraditional binding scaffolds, activatable antibodies, bispecific antibodies, immunocytokines, antibody-drug conjugates, enzymes, polynucleotides, and therapeutic cells, as well as alternative drug carriers such as nanoparticles. Drug development is expensive, attrition rates are high, and efficacy rates are lower than desired. Almost all these drugs, which in general have a long residence time in the body, can stably be labeled with ⁸⁹Zr for whole-body PET imaging and quantification. Although not restricted to monoclonal antibodies, this approach is called ⁸⁹Zr-immuno-PET. This review summarizes the state of the art of the technical aspects of ⁸⁹Zr-immuno-PET and illustrates why it has potential for steering the design, development, and application of biologic drugs. Appealing showcases are discussed to illustrate what can be learned with this emerging technology during preclinical and especially clinical studies about biologic drug formats and disease targets. In addition, an overview of ongoing and completed clinical trials is provided. Although ⁸⁹Zr-immuno-PET is a young tool in drug development, its application is rapidly expanding, with first clinical experiences giving insight on why certain drug-target combinations might have better perspectives than others.

Linked Pharmacometric-Pharmacoeconomic Modeling and Simulation in Clinical Drug Development

Market access and pricing of pharmaceuticals are increasingly contingent on the ability to demonstrate comparative effectiveness and cost-effectiveness. As such, it is widely recognized that predictions of the economic potential of drug candidates in development could inform decisions across the product life cycle. This may be challenging when safety and efficacy profiles in terms of the relevant clinical outcomes are unknown or highly uncertain early in product development. Linking pharmacometrics and pharmacoeconomics, such that outputs from pharmacometric models serve as inputs to pharmacoeconomic models, may provide a framework for extrapolating from early-phase studies to predict economic outcomes and characterize decision uncertainty. This article reviews the published studies that have implemented this methodology and used simulation to inform drug development decisions and/or to optimize the use of drug treatments. Some of the key practical issues involved in linking pharmacometrics and pharmacoeconomics, including the choice of final outcome measures, methods of incorporating evidence on comparator treatments, approaches to handling multiple intermediate end points, approaches to quantifying uncertainty, and issues of model validation are also discussed. Finally, we have considered the potential barriers that may have limited the adoption of this methodology and suggest that closer alignment between the disciplines of clinical pharmacology, pharmacometrics, and pharmacoeconomics, may help to realize the potential benefits associated with linked pharmacometric-pharmacoeconomic modeling and simulation.

Patient-Derived Xenograft vs. Organoids: A Preliminary Analysis of Cancer Research Output, Funding and Human Health Impact in 2014-2019

Cancer remains a major threat to mortality and morbidity globally, despite intense research and generous funding. Patient-derived xenograft (PDX) models-where tumor biopsies are injected into an animal-were developed to improve the predictive capacity of preclinical animal models. However, recent observations have called into question the clinical relevance, and therefore the translational accuracy, of these. Patient-derived organoids (PDO) use patient tumor samples to create in vitro models that maintain aspects of tumor structure and heterogeneity. We undertook a preliminary analysis of the number of breast, colorectal, and lung cancer research studies using PDX or PDO published worldwide between 2014-2019. We looked for evidence of impacts of this research on human health. The number of publications that focused on PDO is gradually increasing over time, but is still very low compared to publications using PDX models. Support for new research projects using PDO is gradually increasing, a promising indicator of a shift towards more human-relevant approaches to understanding human disease. Overall, increases in total funding for these three major cancer types does not appear to be translating to any consequential increase in outputs, defined for this purpose as publications associated with clinical trials. With increasing public discomfort in research using animals and demands for 'alternative' methods, it is timely to consider how to implement non-animal methods more effectively.

Combining experimental strategies for successful target deconvolution

Investment in phenotypic drug discovery has led to increased demand for rapid and robust target deconvolution to aid successful drug development. Although methods for target identification and mechanism of action (MoA) discovery are flourishing, they typically lead to lists of putative targets. Validating which target(s) are involved in the therapeutic mechanism of a compound poses a significant challenge, requiring direct binding, target engagement, and functional studies in relevant physiological contexts. A combination of orthogonal approaches can allow target identification beyond the proteome as well as aid prioritisation for resource-intensive target validation studies.

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.

Levelling the Translational Gap for Animal to Human Efficacy Data

Reports of a reproducibility crisis combined with a high attrition rate in the pharmaceutical industry have put animal research increasingly under scrutiny in the past decade. Many researchers and the general public now question whether there is still a justification for conducting animal studies. While criticism of the current modus operandi in preclinical research is certainly warranted, the data on which these discussions are based are often unreliable. Several initiatives to address the internal validity and reporting quality of animal studies (e.g., Animals in Research: Reporting In Vivo Experiments (ARRIVE) and Planning Research and Experimental Procedures on Animals: Recommendations for Excellence (PREPARE) guidelines) have been introduced but seldom implemented. As for external validity, progress has been virtually absent. Nonetheless, the selection of optimal animal models of disease may prevent the conducting of clinical trials, based on unreliable preclinical data. Here, we discuss three contributions to tackle the evaluation of the predictive value of animal models of disease themselves. First, we developed the Framework to Identify Models of Disease (FIMD), the first step to standardise the assessment, validation and comparison of disease models. FIMD allows the identification of which aspects of the human disease are replicated in the animals, facilitating the selection of disease models more likely to predict human response. Second, we show an example of how systematic reviews and meta-analyses can provide another strategy to discriminate between disease models quantitatively. Third, we explore whether external validity is a factor in animal model selection in the Investigator's Brochure (IB), and we use the IB-derisk tool to integrate preclinical pharmacokinetic and pharmacodynamic data in early clinical development. Through these contributions, we show how we can address external validity to evaluate the translatability and scientific value of animal models in drug development. However, while these methods have potential, it is the extent of their adoption by the scientific community that will define their impact. By promoting and adopting high quality study design and reporting, as well as a thorough assessment of the translatability of drug efficacy of animal models of disease, we will have robust data to challenge and improve the current animal research paradigm.