Structural Approach to Assessing the Innovativeness of New Drugs Finds Accelerating Rate of Innovation

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.

Removing Neighboring Ring Influence in Monocyclic B-OH Diazaborines: Properties and Reactivity as Phenolic Bioisosteres with Dynamic Hydroxy Exchange

The design of small molecules with unique geometric profiles or molecular connectivity represents an intriguing yet neglected challenge in modern organic synthesis. This challenge is compounded when emphasis is placed on the preparation of new chemotypes that have distinct and practical functions. To expand the structural diversity of boron-containing heterocycles, we report herein the preparation of novel monocyclic hemiboronic acids, diazaborines. These compounds have enabled the study of a pseudoaromatic boranol-containing (B-OH) ring free of influence from an appended aromatic system. Synthetic and spectroscopic studies have provided insight into the aromatic character, Lewis acidic nature, chemical reactivity, and unique ability of the exocyclic B-OH unit to participate in hydroxy exchange, suggesting their use in organocatalysis and as reversible covalent inhibitors. Moreover, density functional theory and nucleus-independent chemical shift calculations reveal that the aromatic character of the boroheterocyclic ring is increased significantly in comparison to known bicyclic benzodiazaborines (naphthoid congeners), consequently leading to attenuated Lewis acidity. Direct structural comparison to a well-established biaryl isostere, 2-phenylphenol, through X-ray crystallographic analysis reveals that N-aryl derivatives are strikingly similar in size and conformation, with attenuated logP values underscoring the value of the polar BNN unit. Their potential application as low-molecular-weight scaffolds in drug discovery is demonstrated through orthogonal diversification and preliminary antifungal evaluation (Candida albicans), which unveiled analogs with low micromolar inhibitory concentration.

CNS Drug Discovery in Academia: Where Basic Research Meets Innovation

The involvement of academic research in drug discovery is consistently growing. However, academic projects seldom advance to clinical trials. Here, we assess the landscape of drug discovery within the National Centre of Competence in Research (NCCR) TransCure launched by the Swiss National Science Foundation to foster basic research and early-stage drug discovery on membrane transporters. This included transporters in central nervous system (CNS) disorders, which represent a huge unmet medical need. While idea championship, sustainable funding, collaborations between disciplines at the interface of academia and industry are important for translational research, Popperian falsifiability, strong intellectual property and a motivated startup team are key elements for innovation. This is exemplified by the NCCR TransCure spin-off company Synendos Therapeutics, a clinical stage biotech company developing the first selective endocannabinoid reuptake inhibitors (SERIs) as novel treatment for neuropsychiatric disorders. We provide a perspective on the challenges related to entering an uncharted druggable space and bridging the often mentioned "valley of death". The high attrition rate of drug discovery projects in the CNS field within academia is often due to the lack of meaningful animal models that can provide pharmacological proof-of-concept for potentially disruptive technologies at the earliest stages, and the absence of solid intellectual property.

Exploring SureChEMBL from a drug discovery perspective

In the pharmaceutical industry, the patent protection of drugs and medicines is accorded importance because of the high costs involved in the development of novel drugs. Over the years, researchers have analyzed patent documents to identify freedom-to-operate spaces for novel drug candidates. To assist this, several well-established public patent document data repositories have enabled automated methodologies for extracting information on therapeutic agents. In this study, we delve into one such publicly available patent database, SureChEMBL, which catalogues patent documents related to life sciences. Our exploration begins by identifying patent compounds across public chemical data resources, followed by pinpointing sections in patent documents where the chemical annotations were found. Next, we exhibit the potential of compounds to serve as drug candidates by evaluating their conformity to drug-likeness criteria. Lastly, we examine the drug development stage reported for these compounds to understand their clinical success. In summary, our investigation aims at providing a comprehensive overview of the patent compounds catalogued in SureChEMBL, assessing their relevance to pharmaceutical drug discovery.

KinomeMETA: meta-learning enhanced kinome-wide polypharmacology profiling

Kinase inhibitors are crucial in cancer treatment, but drug resistance and side effects hinder the development of effective drugs. To address these challenges, it is essential to analyze the polypharmacology of kinase inhibitor and identify compound with high selectivity profile. This study presents KinomeMETA, a framework for profiling the activity of small molecule kinase inhibitors across a panel of 661 kinases. By training a meta-learner based on a graph neural network and fine-tuning it to create kinase-specific learners, KinomeMETA outperforms benchmark multi-task models and other kinase profiling models. It provides higher accuracy for understudied kinases with limited known data and broader coverage of kinase types, including important mutant kinases. Case studies on the discovery of new scaffold inhibitors for membrane-associated tyrosine- and threonine-specific cdc2-inhibitory kinase and selective inhibitors for fibroblast growth factor receptors demonstrate the role of KinomeMETA in virtual screening and kinome-wide activity profiling. Overall, KinomeMETA has the potential to accelerate kinase drug discovery by more effectively exploring the kinase polypharmacology landscape.

Expanding the Role of Boron in New Drug Chemotypes: Properties, Chemistry, Pharmaceutical Potential of Hemiboronic Naphthoids

New chemotypes and bioisosteres can open a new chemical space in drug discovery and help meet an urgent demand for novel agents to fight infections and other diseases. With the aim of identifying new boron-containing drug chemotypes, this article details a comprehensive evaluation of the pseudoaromatic hemiboronic naphthoids, benzoxaza- and benzodiazaborines. Relevant physical properties in aqueous media (acidity, solubility, log P, and stability) of prototypic members of four subclasses were determined. Both scaffolds are amenable to common reactions used in drug discovery, such as chemoselective Suzuki-Miyaura, Chan-Lam, and amidation reactions. Small model libraries were prepared to assess the scope of these transformations, and the entire collection was screened for antifungal (Candida albicans) and antibacterial activity (MRSA, Escherichia coli), unveiling promising benzoxazaborines with low micromolar minimum inhibitory concentration values. Select DMPK assays of representative compounds suggest promising drug-like behavior for all four subclasses. Moreover, several drug isosteres were evaluated for anti-inflammatory and anticancer activity as appropriate.

Diversifying chemical space of DNA-encoded libraries: synthesis of 2-oxa-1-azaspiro(bicyclo[3.2.0])heptanes on-DNA via visible light-mediated energy transfer catalysis

Azaspiro[3.3]heptanes are valuable synthetic targets for drug discovery programs. The challenges associated with the preparation and diversification of this moiety as compared to other small, saturated rings have led to limited applications of compounds containing this spirocycle. In this regard, important advances in the field of synthetic photochemistry have exploited the biradical nature of the triplet excited state of 2-isoxazoline-3-carboxylates, engaging these species in intermolecular coupling reactions under visible light irradiation. As a continuation of our program preparing F(sp3)-rich, structurally complex molecules for DNA-encoded library technology (DELT) applications via photocatalysis, we disclose herein the incorporation of unique and densely functionalized 2-oxa-1-azabicyclo[3.2.0]heptanes via [2+2] cycloaddition energy transfer sensitization, providing access to an unexplored library of azaspiro compounds, many of which include additional synthetic handles important for further functionalization of the DNA-conjugated products and for library production.

Molecular Framework Analysis of the Generated Database GDB-13s

The generated databases (GDBs) list billions of possible molecules from systematic enumeration following simple rules of chemical stability and synthetic feasibility. To assess the originality of GDB molecules, we compared their Bemis and Murcko molecular frameworks (MFs) with those in public databases. MFs result from molecules by converting all atoms to carbons, all bonds to single bonds, and removing terminal atoms iteratively until none remain. We compared GDB-13s (99,394,177 molecules up to 13 atoms containing simplified functional groups, 22,130 MFs) with ZINC (885,905,524 screening compounds, 1,016,597 MFs), PubChem50 (100,852,694 molecules up to 50 atoms, 1,530,189 MFs), and COCONUT (401,624 natural products, 42,734 MFs). While MFs in public databases mostly contained linker bonds and six-membered rings, GDB-13s MFs had diverse ring sizes and ring systems without linker bonds. Most GDB-13s MFs were exclusive to this database, and many were relatively simple, representing attractive targets for synthetic chemistry aiming at innovative molecules.

The Time and Place for Nature in Drug Discovery

The case for a renewed focus on Nature in drug discovery is reviewed; not in terms of natural product screening, but how and why biomimetic molecules, especially those produced by natural processes, should deliver in the age of artificial intelligence and screening of vast collections both in vitro and in silico. The declining natural product-likeness of licensed drugs and the consequent physicochemical implications of this trend in the context of current practices are noted. To arrest these trends, the logic of seeking new bioactive agents with enhanced natural mimicry is considered; notably that molecules constructed by proteins (enzymes) are more likely to interact with other proteins (e.g., targets and transporters), a notion validated by natural products. Nature's finite number of building blocks and their interactions necessarily reduce potential numbers of structures, yet these enable expansion of chemical space with their inherent diversity of physical characteristics, pertinent to property-based design. The feasible variations on natural motifs are considered and expanded to encompass pseudo-natural products, leading to the further logical step of harnessing bioprocessing routes to access them. Together, these offer opportunities for enhancing natural mimicry, thereby bringing innovation to drug synthesis exploiting the characteristics of natural recognition processes. The potential for computational guidance to help identifying binding commonalities in the route map is a logical opportunity to enable the design of tailored molecules, with a focus on "organic/biological" rather than purely "synthetic" structures. The design and synthesis of prototype structures should pay dividends in the disposition and efficacy of the molecules, while inherently enabling greener and more sustainable manufacturing techniques.

Carboranes in drug discovery, chemical biology and molecular imaging

There exists a paucity of structural innovation and limited molecular diversity associated with molecular frameworks in drug discovery and biomolecular imaging/chemical probe design. The discovery and exploitation of new molecular entities for medical and biological applications will necessarily involve voyaging into previously unexplored regions of chemical space. Boron clusters, notably the carboranes, offer an alternative to conventional (poly)cyclic organic frameworks that may address some of the limitations associated with the use of novel molecular frameworks in chemical biology or medicine. The high thermal stability, unique 3D structure and aromaticity, kinetic inertness to metabolism and ability to engage in unusual types of intermolecular interactions, such as dihydrogen bonds, with biological receptors make carboranes exquisite frameworks in the design of probes for chemical biology, novel drug candidates and biomolecular imaging agents. This Review highlights the key developments of carborane derivatives made over the last decade as new design tools in medicinal chemistry and chemical biology, showcasing the versatility of this unique family of boron compounds.

Rings in Clinical Trials and Drugs: Present and Future

We present a comprehensive analysis of all ring systems (both heterocyclic and nonheterocyclic) in clinical trial compounds and FDA-approved drugs. We show 67% of small molecules in clinical trials comprise only ring systems found in marketed drugs, which mirrors previously published findings for newly approved drugs. We also show there are approximately 450 000 unique ring systems derived from 2.24 billion molecules currently available in synthesized chemical space, and molecules in clinical trials utilize only 0.1% of this available pool. Moreover, there are fewer ring systems in drugs compared with those in clinical trials, but this is balanced by the drug ring systems being reused more often. Furthermore, systematic changes of up to two atoms on existing drug and clinical trial ring systems give a set of 3902 future clinical trial ring systems, which are predicted to cover approximately 50% of the novel ring systems entering clinical trials.

A model for cooperative scientific research inspired by the ant colony algorithm

Modern scientific research has become largely a cooperative activity in the Internet age. We build a simulation model to understand the population-level creativity based on the heuristic ant colony algorithm. Each researcher has two heuristic parameters characterizing the goodness of his own judgments and his trust on literature. We study how the distributions of contributor heuristic parameters change with the research problem scale, stage of the research problem, and computing power available. We also identify situations where path dependence and hasty research due to the pressure on productivity can significantly impede the long-term advancement of scientific research. Our work provides some preliminary understanding and guidance for the dynamical process of cooperative scientific research in various disciplines.

Opportunities and Considerations in the Application of Artificial Intelligence to Pharmacokinetic Prediction

The improvement in the ability of the pharmaceutical industry to predict human pharmacokinetic behavior are attributable to major technological shifts from 1990 to the present day. The opportunity for the application of AI/ML based approaches in the pharmaceutical industry is driven by the abundance of data sets that exist within individual pharmaceutical and biotech companies and the availability, within these environments, of abundant computing power. This chapter seeks to describe opportunities for artificial intelligence to contribute to the assessment and evaluation of the dug metabolism and pharmacokinetic (DMPK) properties of novel compounds across the drug discovery and development continuum. Many initiatives are already underway with respect to the application of AI/ML in predicting pharmacokinetic profiles so the question is not whether AI will influence pharmacokinetic prediction but rather how to best utilize and incorporate this and how to evaluate the value added from these applications. Since our understanding of the underlying biology of the in vitro and in vivo systems with respect to ADME, one of the key challenges to AI-based methods will be the ability to adapt to data sets that change in quality over time.

Target-Based Evaluation of "Drug-Like" Properties and Ligand Efficiencies

Physicochemical descriptors commonly used to define "drug-likeness" and ligand efficiency measures are assessed for their ability to differentiate marketed drugs from compounds reported to bind to their efficacious target or targets. Using ChEMBL version 26, a data set of 643 drugs acting on 271 targets was assembled, comprising 1104 drug-target pairs having ≥100 published compounds per target. Taking into account changes in their physicochemical properties over time, drugs are analyzed according to their target class, therapy area, and route of administration. Recent drugs, approved in 2010-2020, display no overall differences in molecular weight, lipophilicity, hydrogen bonding, or polar surface area from their target comparator compounds. Drugs are differentiated from target comparators by higher potency, ligand efficiency (LE), lipophilic ligand efficiency (LLE), and lower carboaromaticity. Overall, 96% of drugs have LE or LLE values, or both, greater than the median values of their target comparator compounds.

Pharmacometabonomics: data processing and statistical analysis

Individual variations in drug efficacy, side effects and adverse drug reactions are still challenging that cannot be ignored in drug research and development. The aim of pharmacometabonomics is to better understand the pharmacokinetic properties of drugs and monitor the drug effects on specific metabolic pathways. Here, we systematically reviewed the recent technological advances in pharmacometabonomics for better understanding the pathophysiological mechanisms of diseases as well as the metabolic effects of drugs on bodies. First, the advantages and disadvantages of all mainstream analytical techniques were compared. Second, many data processing strategies including filtering, missing value imputation, quality control-based correction, transformation, normalization together with the methods implemented in each step were discussed. Third, various feature selection and feature extraction algorithms commonly applied in pharmacometabonomics were described. Finally, the databases that facilitate current pharmacometabonomics were collected and discussed. All in all, this review provided guidance for researchers engaged in pharmacometabonomics and metabolomics, and it would promote the wide application of metabolomics in drug research and personalized medicine.