Most clinical trials are delayed due to scientific and/or operational challenges. Any effort to minimize delays can generate value for patients and sponsors. This article reviews critical path process steps commonly identified by practitioners, such as during protocol development, site contracting, or patient recruitment. Commonly considered measures, such as adding more trial sites or countries, were contrasted with less frequented measures, such as evidence-based feasibility or real-world evidence analysis, to help validate assumptions before clinical trial initiation. In a broad analysis, we integrated a literature review with a practitioner survey into a framework to help decision makers on the most critical process steps when setting up or conducting clinical trials in order to bring critical treatments to patients faster.

The complement system is involved in the pathogenesis of several ocular diseases, providing a rationale for the investigation of complement-targeting therapeutics for these conditions. Dry age-related macular degeneration, as characterised by geographic atrophy (GA), is currently the most active area of research for complement-targeting therapeutics, with a complement C3 inhibitor approved in the United States earlier this year marking the first approved therapy for GA. This review discusses the role of complement in ocular disease, provides an overview of the complement-targeting agents currently under development for ocular conditions, and reflects on the lessons that can be learned from the preclinical investigations and clinical trials conducted in this field to date.

Diabetic nephropathy (DN) is a dreadful complication of diabetes that affects ∼50% of diabetics and is a leading cause of end-stage renal disease (ESRD). Studies have linked aberrant expression of lysine methyltransferases (KMTs) to the onset and progression of DN. SET7 is a KMT that methylates specific lysine residues of the histone and nonhistone proteins. It plays an important role in the transforming growth factor-β (TGF-β)-induced upregulation of extracellular matrix (ECM)-associated genes that are responsible for the inflammatory cascade observed in DN. Inhibiting SET7 has potential to attenuate renal disorders in animal studies. This review will focus on the role of SET7 in DN and its potential as a therapeutic target to combat DN.

Rare disease (RD) patients face significant unmet therapeutic needs worldwide. However, orphan drugs approved in the United States, but not approved or developed in Japan, have increased rapidly with recently increasing US approvals, indicating greater RD drug loss in Japan. US/EU-based startups have become key players in RD drug R&D, significantly contributing to this drug loss trend. They successfully develop drugs in the United States by combining in-licensing with in-house drug discovery. Out-licensing to Japanese companies or large pharma is critical for expansion into Japan, with successes attributed to drug innovation, target indications, and transactional capabilities. These findings highlight the need to foster partnerships with startups and cultivate an ecosystem in Japan that nurtures local startups, to address drug loss and ensure access to promising drugs.

Metabolomics and lipidomics have an increasingly pivotal role in drug discovery and development. In the context of drug discovery, monitoring changes in the levels or composition of metabolites and lipids relative to genetic variations yields functional insights, bolstering human genetics and (meta)genomic methodologies. This approach also sheds light on potential novel targets for therapeutic intervention. In the context of drug development, metabolite and lipid biomarkers contribute to enhanced success rates, promising a transformative impact on precision medicine. In this review, we deviate from analytical chemist-focused perspectives, offering an overview tailored to drug discovery. We provide introductory insight into state-of-the-art mass spectrometry (MS)-based metabolomics and lipidomics techniques utilized in drug discovery and development, drawing from the collective expertise of our research teams. We comprehensively outline the application of metabolomics and lipidomics in advancing drug discovery and development, spanning fundamental research, target identification, mechanisms of action, and the exploration of biomarkers.

Existing antibacterial agents can be categorized into two generations, but bacterial insensitivity towards both of these generations poses a serious public health challenge worldwide. Thus, novel approaches and/or novel antibacterials are urgently needed to maintain a concentration of antibacterials that is lethal to bacteria that are resistant to existing antibiotic treatments. Metabolite(s)-based adjuvants that promote antibiotic uptake and enhance antibiotic efficacy are an effective strategy that is unlikely to develop resistance. Thus, we propose a metabolite(s)-based approach, in which metabolites and antibacterials are combined, as a promising strategy for the development of next-generation agents to combat a variety of antibiotic-resistant pathogens.

Inflammation and cell death processes positively control the organ homeostasis of an organism. Receptor-interacting protein kinase 1 (RIPK1), a member of the RIPK family, is a crucial regulator of cell death and inflammation, and control homeostasis at the cellular and tissue level. Necroptosis, a programmed form of necrosis-mediated cell death and tumor necrosis factor (TNF)-induced necrotic cell death, is mostly regulated by RIPK1 kinase activity. Thus, RIPK1 has recently emerged as an upstream kinase that controls multiple cellular pathways and participates in regulating inflammation and cell death. All the major cell types in the central nervous system (CNS) have been found to express RIPK1. Selective inhibition of RIPK1 has been shown to prevent neuronal cell death, which could ultimately lead to a significant reduction of neurodegeneration and neuroinflammation. In addition, the kinase structure of RIPK1 is highly conducive to the development of specific pharmacological small-molecule inhibitors. These factors have led to the emergence of RIPK1 as an important therapeutic target for Alzheimer's disease (AD).

Precision medicine requires selecting the appropriate dosage regimen for a patient using the right drug, at the right time. Model-Informed Precision Dosing (MIPD) is a concept suggesting utilization of model-based prediction methods for optimizing the treatment benefit-harm balance, based on individual characteristics of the patient, disease, treatment method, and other factors. Here, we discuss a theoretical workflow comprising several elements, beginning from the physiologically based pharmacokinetic/pharmacodynamic (PBPK/PD) models, through 3D printed tablets with the model proposed dose, information range and flow, and the patient themselves. We also describe each of these elements, and the connection between them, highlighting challenges and potential obstacles.

External innovation initiatives in the pharmaceutical industry have become an integral part of research and development. Collaborations have been built to enhance innovation, mitigate risk, and share cost, especially for neurodegenerative diseases, a therapeutic area that has suffered from high attrition rates. This article outlines the Eisai-University College London (UCL) Drug Discovery and Development Collaboration as a case study of how to implement a productive industry-academic partnership. In the first 10 years, seven projects have been established and the first project, a novel anti-tau antibody for Alzheimer's disease, has entered clinical trials, providing early validation of this collaboration model.

We describe a roadmap for a fully digital artificial intelligence (AI)-augmented nonclinical pathology laboratory across three continents. Underpinning the design are Good Laboratory Practice (GLP)-validated laboratory information management systems (LIMS), whole slide-scanners (WSS), image management systems (IMS), and a digital microscope intended for use by the nonclinical pathologist. Digital diagnostics are supported by tools that include AI-based virtual staining and deep learning-based decision support. Implemented during the COVID-19 pandemic, the initial digitized workflow largely mitigated disruption of pivotal nonclinical studies required to support pharmaceutical clinical testing. We believe that this digital transformation of our nonclinical pathology laboratories will promote efficiency and innovation in the future and enhance the quality and speed of drug development decision making.

To discover new drugs is to seek and to prove causality. As an emerging approach leveraging human knowledge and creativity, data, and machine intelligence, causal inference holds the promise of reducing cognitive bias and improving decision-making in drug discovery. Although it has been applied across the value chain, the concepts and practice of causal inference remain obscure to many practitioners. This article offers a nontechnical introduction to causal inference, reviews its recent applications, and discusses opportunities and challenges of adopting the causal language in drug discovery and development.

Human serum albumin (HSA) is the most abundant protein in the blood and has desirable properties as a drug carrier. One of the most promising ways to exploit HSA as a carrier is to append an albumin-binding moiety (ABM) to a drug for in situ HSA binding upon administration. Nature- and library-derived ABMs vary in size, affinity, and epitope, differentially improving the pharmacokinetics of an appended drug. In this review, we evaluate the current state of knowledge regarding various aspects of ABMs and the unique advantages of ABM-mediated drug delivery. Furthermore, we discuss how ABMs can be specifically modulated to maximize potential benefits in clinical development.

Preclinical toxicity assessments of new drugs require the use of in silico prediction techniques as ethics, cost, time, and complexity limit in vitro and in vivo methods. This review discusses the fundamental concepts of biophores especially toxicophores and their detection methodologies, tools and techniques, as well as ongoing challenges, and methods for overcoming them. This will guide the design community in manipulating lead compounds via a pre-determined pathway based on the MeMeReMe approach. The ideas discussed will be useful both for predicting toxicity and for de-risking leads through optimization.

The blood-brain barrier (BBB) is a protective element of the neurovascular unit (NVU) surrounded by astrocytes, pericytes, extracellular matrix, and the tight junctional complex, which play a fundamental role in brain homeostasis. Due to its impeccable structural architecture, the BBB is referred to as the brain's gatekeeper, a near-impenetrable barrier to therapeutics. This review summarises the significant strides that have been made in the last 5 years towards circumventing the BBB and developing efficient drug delivery systems. Challenges associated with several CNS disorders related to BBB failure and exploitation of this unique NVU component for targeted treatment of brain-related disorders are also discussed.

Effective portfolio management is crucial for innovation and sustaining revenue in pharmaceutical companies. This article holistically reviews trends, challenges, and approaches to pharmaceutical portfolio management and focuses, in particular, on cognitive biases in portfolio decision-making. Portfolio managers strongly rely on external innovation and face increasing competitive pressure and portfolio complexity. The ability to address biases and make robust decisions remains a challenge. Portfolio management practitioners most commonly face confirmation bias, champion bias, or misaligned incentives, which they seek to mitigate through expert input, team diversity, and rewarding truth-seeking. Ultimately, highest-quality portfolio management decision-making could be enabled by three factors: high-quality data, structured review processes, and comprehensive mitigating measures against biases in decision-making.

In this review, we outline recent advancements in small molecule drug design from a structural perspective. We compare protein structure prediction methods and explore the role of the ligand binding pocket in structure-based drug design. We examine various structural features used to optimize drug candidates, including functional groups, stereochemistry, and molecular weight. Computational tools such as molecular docking and virtual screening are discussed for predicting and optimizing drug candidate structures. We present examples of drug candidates designed based on their molecular structure and discuss future directions in the field. By effectively integrating structural information with other valuable data sources, we can improve the drug discovery process, leading to the identification of novel therapeutics with improved efficacy, specificity, and safety profiles.