A new paradigm for navigating compound property related drug attrition

Electrophysiological Changes in the Rabbit Ventricular Wedge and Human-Induced Pluripotent Stem-Cell Derived (IPSC) Cardiomyocytes Translate to Severe Arrhythmia Observed in a Canine Toxicology Study, Not Predicted by Standard In Vitro Ion Channel Assays

During drug discovery, small molecules are typically assayed in vitro for secondary pharmacology effects, which include ion channels relevant to cardiac electrophysiology. Compound A was an irreversible inhibitor of myeloperoxidase investigated for the treatment of peripheral artery disease. Oral doses in dogs at ≥5 mg/kg resulted in cardiac arrhythmias in a dose-dependent manner (at Cmax, free ≥1.53 μM) that progressed in severity with time. Nevertheless, a panel of 13 different cardiac ion channel (K, Na, and Ca) assays, including hERG, failed to identify pharmacologic risks of the molecule. Compound A and a related Compound B were evaluated for electrophysiological effects in the isolated rabbit ventricular wedge assay. Compounds A and B prolonged QT and Tp-e intervals at ≥1 and ≥.3 μM, respectively, and both prolonged QRS at ≥5 μM. Compound A produced early after depolarizations and premature ventricular complexes at ≥5 μM. These data indicate both compounds may be modulating hERG (Ikr) and Nav1.5 ion channels. In human IPSC cardiomyocytes, Compounds A and B prolonged field potential duration at ≥3 μM and induced cellular dysrhythmia at ≥10 and ≥3 μM, respectively. In a rat toxicology study, heart tissue: plasma concentration ratios for Compound A were ≥19X at 24 hours post-dose, indicating significant tissue distribution. In conclusion, in vitro ion channel assays may not always identify cardiovascular electrophysiological risks observed in vivo, which can be affected by tissue drug distribution. Risk for arrhythmia may increase with a "trappable" ion channel inhibitor, particularly if cardiac tissue drug levels achieve a critical threshold for pharmacologic effects.

Interaction between Permeation Enhancers and Lipid Bilayers

Permeation enhancers (PEs) are a class of molecules that interact with the epithelial membrane and transiently increase its transcellular permeability. Although there have been few clinical trials of PE coformulated drugs, the mechanism of action of PEs remains elusive. In this paper, the interaction between two archetypes of PEs [salcaprozate sodium (SNAC) and sodium caprate (C10)] and membranes is investigated with extensive all-atom molecular dynamics simulations. The simulations show that (1) the association between the neutral PEs and membranes is favored in free energy, (2) the propensity of neutral PE aggregation is larger in aqueous solution than in lipid bilayers, (3) the equilibrium distribution of neutral PEs in membranes is fast, e.g., accessible with unbiased MD simulations, and (4) the micelle of neutral PEs formed in aqueous solution does not rupture the membranes (e.g., not forming pores or breaking up the membrane) under simulation conditions. All results combined, this study indicates that PEs insert into the membranes in an equilibrium or near equilibrium process. This study lays the foundation for future investigations of how PEs impact the free energy of permeation for small molecules.

Application of message passing neural networks for molecular property prediction

Accurate molecular property prediction, as one of the classical cheminformatics topics, plays a prominent role in the fields of computer-aided drug design. For instance, property prediction models can be used to quickly screen large molecular libraries to find lead compounds. Message-passing neural networks (MPNNs), a sub-class of Graph neural networks (GNNs), have recently been demonstrated to outperform other deep learning methods on a variety of tasks, including the prediction of molecular characteristics. In this survey, we provide a brief review of the MPNN models and their applications on molecular property prediction.

Rewiring Drug Research and Development through Human Data-Driven Discovery (HD3)

In an era of unparalleled technical advancement, the pharmaceutical industry is struggling to transform data into increased research and development efficiency, and, as a corollary, new drugs for patients. Here, we briefly review some of the commonly discussed issues around this counterintuitive innovation crisis. Looking at both industry- and science-related factors, we posit that traditional preclinical research is front-loading the development pipeline with data and drug candidates that are unlikely to succeed in patients. Applying a first principles analysis, we highlight the critical culprits and provide suggestions as to how these issues can be rectified through the pursuit of a Human Data-driven Discovery (HD³) paradigm. Consistent with other examples of disruptive innovation, we propose that new levels of success are not dependent on new inventions, but rather on the strategic integration of existing data and technology assets. In support of these suggestions, we highlight the power of HD³, through recently published proof-of-concept applications in the areas of drug safety analysis and prediction, drug repositioning, the rational design of combination therapies and the global response to the COVID-19 pandemic. We conclude that innovators must play a key role in expediting the path to a largely human-focused, systems-based approach to drug discovery and research.

Forces Driving a Magic Bullet to Its Target: Revisiting the Role of Thermodynamics in Drug Design, Development, and Optimization

Drug discovery strategies have advanced significantly towards prioritizing target selectivity to achieve the longstanding goal of identifying “magic bullets” amongst thousands of chemical molecules screened for therapeutic efficacy. A myriad of emerging and existing health threats, including the SARS-CoV-2 pandemic, alarming increase in bacterial resistance, and potentially fatal chronic ailments, such as cancer, cardiovascular disease, and neurodegeneration, have incentivized the discovery of novel therapeutics in treatment regimens. The design, development, and optimization of lead compounds represent an arduous and time-consuming process that necessitates the assessment of specific criteria and metrics derived via multidisciplinary approaches incorporating functional, structural, and energetic properties. The present review focuses on specific methodologies and technologies aimed at advancing drug development with particular emphasis on the role of thermodynamics in elucidating the underlying forces governing ligand–target interaction selectivity and specificity. In the pursuit of novel therapeutics, isothermal titration calorimetry (ITC) has been utilized extensively over the past two decades to bolster drug discovery efforts, yielding information-rich thermodynamic binding signatures. A wealth of studies recognizes the need for mining thermodynamic databases to critically examine and evaluate prospective drug candidates on the basis of available metrics. The ultimate power and utility of thermodynamics within drug discovery strategies reside in the characterization and comparison of intrinsic binding signatures that facilitate the elucidation of structural–energetic correlations which assist in lead compound identification and optimization to improve overall therapeutic efficacy.

Fast-Fed Variability: Insights into Drug Delivery, Molecular Manifestations, and Regulatory Aspects

Among various drug administration routes, oral drug delivery is preferred and is considered patient-friendly; hence, most of the marketed drugs are available as conventional tablets or capsules. In such cases, the administration of drugs with or without food has tremendous importance on the bioavailability of the drugs. The presence of food may increase (positive effect) or decrease (negative effect) the bioavailability of the drug. Such a positive or negative effect is undesirable since it makes dosage estimation difficult in several diseases. This may lead to an increased propensity for adverse effects of drugs when a positive food effect is perceived. However, a negative food effect may lead to therapeutic insufficiency for patients suffering from life-threatening disorders. This review emphasizes the causes of food effects, formulation strategies to overcome the fast-fed variability, and the regulatory aspects of drugs with food effects, which may open new avenues for researchers to design products that may help to eliminate fast-fed variability.

Crowdsourced identification of multi-target kinase inhibitors for RET- and TAU- based disease: The Multi-Targeting Drug DREAM Challenge

A continuing challenge in modern medicine is the identification of safer and more efficacious drugs. Precision therapeutics, which have one molecular target, have been long promised to be safer and more effective than traditional therapies. This approach has proven to be challenging for multiple reasons including lack of efficacy, rapidly acquired drug resistance, and narrow patient eligibility criteria. An alternative approach is the development of drugs that address the overall disease network by targeting multiple biological targets ('polypharmacology'). Rational development of these molecules will require improved methods for predicting single chemical structures that target multiple drug targets. To address this need, we developed the Multi-Targeting Drug DREAM Challenge, in which we challenged participants to predict single chemical entities that target pro-targets but avoid anti-targets for two unrelated diseases: RET-based tumors and a common form of inherited Tauopathy. Here, we report the results of this DREAM Challenge and the development of two neural network-based machine learning approaches that were applied to the challenge of rational polypharmacology. Together, these platforms provide a potentially useful first step towards developing lead therapeutic compounds that address disease complexity through rational polypharmacology.

Ligand binding at the protein-lipid interface: strategic considerations for drug design

Many drug targets are embedded within the phospholipid bilayer of cellular membranes, including G protein-coupled receptors, ion channels, transporters and membrane-bound enzymes. Increasing evidence from biophysical and structural studies suggests that many small-molecule drugs commonly associate with these targets at binding sites at the protein-phospholipid interface. Without a direct path from bulk solvent to a binding site, a drug must first partition in the phospholipid membrane before interacting with the protein target. This membrane access mechanism necessarily affects the interpretation of potency data, structure-activity relationships, pharmacokinetics and physicochemical properties for drugs that target these sites. With an increasing number of small-molecule intramembrane binding sites revealed through X-ray crystallography and cryogenic electron microscopy, we suggest that ligand-lipid interactions likely play a larger role in small-molecule drug action than commonly appreciated. This Perspective introduces key concepts and drug design considerations to aid discovery teams operating within this target space, and discusses challenges and future opportunities in the field.

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.

Intestinal permeation enhancers to improve oral bioavailability of macromolecules: reasons for low efficacy in humans

INTRODUCTION

Intestinal permeation enhancers (PEs) are substances that transiently alter the intestinal epithelial barrier to facilitate permeation of macromolecules with low oral bioavailability (BA). While a number of PEs have progressed to clinical testing in conventional formulations with macromolecules, there has been only low single digit increases in oral BA, irrespective of whether the drug met primary or secondary clinical endpoints.

AREAS COVERED

This article considers the causes of sub-optimal BA of macromolecules from PE dosage forms and suggests approaches that may improve performance in humans.

EXPERT OPINION

Permeation enhancement is most effective when the PE is co-localized with the macromolecule at the epithelial surface. Conditions in the GI tract impede optimal co-localization. Novel delivery systems that limit dilution and spreading of the PE and macromolecule in the small intestine have attempted to replicate promising enhancement efficacy observed in static drug delivery models.

Design, Synthesis, and Physicochemical and Pharmacological Profiling of 7-Hydroxy-5-oxopyrazolo[4,3-b]pyridine-6-carboxamide Derivatives with Antiosteoarthritic Activity In Vivo

The hallmark of joint diseases, such as osteoarthritis (OA), is pain, originating from both inflammatory and neuropathic components, and compounds able to modulate the signal transduction pathways of the cannabinoid type-2 receptor (CB2R) can represent a helpful option in the treatment of OA. In this perspective, a set of 18 cannabinoid type-2 receptor (CB2R) ligands was developed based on an unprecedented structure. With the aim of improving the physicochemical properties of previously reported 4-hydroxy-2-quinolone-3-carboxamides, a structural optimization program led to the discovery of isosteric 7-hydroxy-5-oxopyrazolo[4,3-b]pyridine-6-carboxamide derivatives. These new compounds are endowed with high affinity for the CB2R and moderate to good selectivity over the cannabinoid type-1 receptor (CB1R), associated with good physicochemical characteristics. As to the functional activity at the CB2R, compounds able to act either as agonists or as inverse agonists/antagonists were discovered. Among them, compound 51 emerged as a potent CB2R agonist able to reduce pain in rats carrying OA induced by injection of monoiodoacetic acid (MIA).

Application of Permeation Enhancers in Oral Delivery of Macromolecules: An Update

The application of permeation enhancers (PEs) to improve transport of poorly absorbed active pharmaceutical ingredients across the intestinal epithelium is a widely tested approach. Several hundred compounds have been shown to alter the epithelial barrier, and although the research emphasis has broadened to encompass a role for nanoparticle approaches, PEs represent a key constituent of conventional oral formulations that have progressed to clinical testing. In this review, we highlight promising PEs in early development, summarize the current state of the art, and highlight challenges to the translation of PE-based delivery systems into safe and effective oral dosage forms for patients.

Human liver spheroids in chemically defined conditions for studies of gene–drug, drug–drug and disease–drug interactions

Recent phenotypically and functionally relevant human hepatic in vitro systems combine the ability to preserve interindividual molecular differences between patients’ livers in culture with the accessibility and high-throughput compatibility of in vitro assays. These features facilitate studies of specific genetic polymorphisms by using cells from donors with defined variants of interest or by selective gene knock-down experiments. Furthermore, these models constitute promising tools to evaluate drug–drug interactions as well as the effects of liver diseases on drug pharmacokinetics in co-cultures of hepatocytes and non-parenchymal cells. In the near future, we anticipate that these tools will be of high relevance for predicting the in vivo kinetics, toxicity and drug–drug interactions of drug candidates already during preclinical development.

Extending in Silico Protein Target Prediction Models to Include Functional Effects

In silico protein target deconvolution is frequently used for mechanism-of-action investigations; however existing protocols usually do not predict compound functional effects, such as activation or inhibition, upon binding to their protein counterparts. This study is hence concerned with including functional effects in target prediction. To this end, we assimilated a bioactivity training set for 332 targets, comprising 817,239 active data points with unknown functional effect (binding data) and 20,761,260 inactive compounds, along with 226,045 activating and 1,032,439 inhibiting data points from functional screens. Chemical space analysis of the data first showed some separation between compound sets (binding and inhibiting compounds were more similar to each other than both binding and activating or activating and inhibiting compounds), providing a rationale for implementing functional prediction models. We employed three different architectures to predict functional response, ranging from simplistic random forest models ('Arch1') to cascaded models which use separate binding and functional effect classification steps ('Arch2' and 'Arch3'), differing in the way training sets were generated. Fivefold stratified cross-validation outlined cascading predictions provides superior precision and recall based on an internal test set. We next prospectively validated the architectures using a temporal set of 153,467 of in-house data points (after a 4-month interim from initial data extraction). Results outlined Arch3 performed with the highest target class averaged precision and recall scores of 71% and 53%, which we attribute to the use of inactive background sets. Distance-based applicability domain (AD) analysis outlined that Arch3 provides superior extrapolation into novel areas of chemical space, and thus based on the results presented here, propose as the most suitable architecture for the functional effect prediction of small molecules. We finally conclude including functional effects could provide vital insight in future studies, to annotate cases of unanticipated functional changeover, as outlined by our CHRM1 case study.

Evaluation of the Relevance of DILI Predictive Hypotheses in Early Drug Development: Review of In Vitro Methodologies vs BDDCS Classification

Drug-induced liver injury (DILI) is a major safety concern; it occurs frequently; it is idiosyncratic; it cannot be adequately predicted; and a multitude of underlying mechanisms has been postulated. A number of experimental approaches to predict human DILI have been proposed utilizing in vitro screening such as inhibition of mitochondrial function, hepatobiliary transporter inhibition, reactive metabolite formation with and without covalent binding, and cellular health, but they have achieved only minimal success. Several studies have shown total administered dose alone or in combination with drug lipophilicity to be correlated with a higher risk of DILI. However, it would be best to have a predictive DILI methodology early in drug development, long before the clinical dose is known. Here we discuss the extent to which Biopharmaceutics Drug Disposition Classification System (BDDCS) defining characteristics, independent of knowing actual drug pharmacokinetics/pharmacodynamics and dose, can be used to evaluate prior published predictive proposals. Our results show that BDDCS Class 2 drugs exhibit the highest DILI severity, and that all of the short-lived published methodologies evaluated here, except when daily dose is known, do not yield markedly better predictions than BDDCS. The assertion that extensively metabolized compounds are at higher risk of developing DILI is confirmed, but can be enhanced by differentiating BDDCS Class 2 from Class 1 drugs.

CONCLUSION

Our published analyses suggest that comparison of proposed DILI prediction methodologies with BDDCS classification is a useful tool to evaluate the potential reliability of newly proposed algorithms, although BDDCS classification itself is not sufficiently predictive. Almost all of the predictive DILI metrics do no better than just avoiding BDDCS Class 2 drugs, although some early data with microliver platforms enabling long-enduring metabolic competency show promising results.

Translating Knowledge Into Therapy for Acute Kidney Injury

No therapies have been shown to improve outcomes in patients with acute kidney injury (AKI). Given the high morbidity and mortality associated with AKI this represents an important unmet medical need. A common feature of all of the therapeutic development efforts for AKI is that none were driven by target selection or preclinical modeling that was based primarily on human data. This is important when considering a heterogeneous and dynamic condition such as AKI, in which in the absence of more accurate molecular classifications, clinical cohorts are likely to include patients with different types of injury at different stages in the injury and repair continuum. The National Institutes of Health precision medicine initiative offers an opportunity to address this. By creating a molecular tissue atlas of AKI, defining patient subgroups, and identifying critical cells and pathways involved in human AKI, this initiative has the potential to transform our current approach to therapeutic discovery. In this review, we discuss the opportunities and challenges that this initiative presents, with a specific focus on AKI, what additional efforts will be needed to apply these discoveries to therapeutic development, and how we believe this effort might lead to the development of new therapeutics for subsets of patients with AKI.

In silico ligand-based modeling of hBACE-1 inhibitors

Alzheimer's disease is a chronic neurodegenerative disease affecting more than 30 million people worldwide. Development of small molecule inhibitors of human β-secretase 1 (hBACE-1) is being the focus of pharmaceutical industry for the past 15-20 years. Here, we successfully applied multiple ligand-based in silico modeling techniques to understand the inhibitory activities of a diverse set of small molecule hBACE-1 inhibitors reported in the scientific literature. Strikingly, the use of only a small subset of 230 (13%) molecules allowed us to develop quality models that performed reasonably well on the validation set of 1,476 (87%) inhibitors. Varying the descriptor sets and the complexity of the modeling techniques resulted in only minor improvements to the model's performance. The current results demonstrate that predictive models can be built by choosing appropriate modeling techniques in spite of using small datasets consisting of diverse chemical classes, a scenario typical in triaging of high-throughput screening results to identify false negatives. We hope that these encouraging results will help the community to develop more predictive models that would support research efforts for the debilitating Alzheimer's disease. Additionally, the integrated diversity of the techniques employed will stimulate scientists in the field to use in silico statistical modeling techniques like these to derive better models to help advance the drug discovery projects faster.

Hepatic transporter drug-drug interactions: an evaluation of approaches and methodologies

INTRODUCTION

Drug-drug interactions (DDIs) continue to account for 5% of hospital admissions and therefore remain a major regulatory concern. Effective, quantitative prediction of DDIs will reduce unexpected clinical findings and encourage projects to frontload DDI investigations rather than concentrating on risk management ('manage the baggage') later in drug development. A key challenge in DDI prediction is the discrepancies between reported models. Areas covered: The current synopsis focuses on four recent influential publications on hepatic drug transporter DDIs using static models that tackle interactions with individual transporters and in combination with other drug transporters and metabolising enzymes. These models vary in their assumptions (including input parameters), transparency, reproducibility and complexity. In this review, these facets are compared and contrasted with recommendations made as to their application. Expert opinion: Over the past decade, static models have evolved from simple [I]/k_i models to incorporate victim and perpetrator disposition mechanisms including the absorption rate constant, the fraction of the drug metabolised/eliminated and/or clearance concepts. Nonetheless, models that comprise additional parameters and complexity do not necessarily out-perform simpler models with fewer inputs. Further, consideration of the property space to exploit some drug target classes has also highlighted the fine balance required between frontloading and back-loading studies to design out or 'manage the baggage'.

Ligand efficiency metrics in drug discovery: the pros and cons from a practical perspective

INTRODUCTION

Ligand efficiency metrics are almost universally accepted as a valuable indicator of compound quality and an aid to reduce attrition. Areas covered: In this review, the authors describe ligand efficiency metrics giving a balanced overview on their merits and points of weakness in order to enable the readers to gain an informed opinion. Relevant theoretical breakthroughs and drug-like properties are also illustrated. Several recent exemplary case studies are discussed in order to illustrate the main fields of application of ligand efficiency metrics. Expert opinion: As a medicinal chemist guide, ligand efficiency metrics perform in a context- and chemotype-dependent manner; thus, they should not be used as a magic box. Since the 'big bang' of efficiency metrics occurred more or less ten years ago and the average time to develop a new drug is over the same period, the next few years will give a clearer outlook on the increased rate of success, if any, gained by means of these new intriguing tools.

Derivatives of the Lignan 7'-Hydroxymatairesinol with Antioxidant Properties and Enhanced Lipophilicity

The lignan 7'-hydroxymatairesinol (1), extracted from the knotwoods of fir (Abies alba), spruce (Picea abies), and Douglas fir (Pseudotsuga menziesii), exhibited unexpected reactivity when esterification reactions were attempted on the hydroxy group at position C-7'. To circumvent the rapid intramolecular cyclization procedure, leading quantitatively to the lignan conidendrin (7), a simple strategy for 7'-esterification of 1 under mild conditions (three steps, up to 80% overall yield) was developed. Compared to hydroxymatairesinol (1) (log K'_w = 1.49), the derivatives (2-5) had increased lipophilicity with log K'_w > 3.1, as determined by a UHPLC method. Compounds 1-5 exhibited potent antioxidant properties in the same range as the standards ascorbic acid and α-tocopherol (IC₅₀ = 20-25 μM) and higher than that of BHT using a DPPH radical-scavenging assay.

Pharmacological property optimization for allosteric ligands: A medicinal chemistry perspective

New strategies to potentially improve drug safety and efficacy emerge with allosteric programs. Biased allosteric modulators can be designed with high subtype selectivity and defined receptor signaling endpoints, however, selecting the most meaningful parameters for optimization can be perplexing. Historically, "potency hunting" at the expense of physicochemical and pharmacokinetic optimization has led to numerous tool compounds with excellent pharmacological properties but no path to drug development. Conversely, extensive physicochemical and pharmacokinetic screening with only post hoc bias and allosteric characterization has led to inefficacious compounds or compounds with on-target toxicities. This field is rapidly evolving with new mechanistic understanding, changes in terminology, and novel opportunities. The intent of this digest is to summarize current understanding and debates within the field. We aim to discuss, from a medicinal chemistry perspective, the parameter choices available to drive SAR.

Discovery of New Chemical Entities for Old Targets: Insights on the Lead Optimization of Chromone-Based Monoamine Oxidase B (MAO-B) Inhibitors

The discovery of new chemical entities endowed with potent, selective, and reversible monoamine oxidase B inhibitory activity is a clinically relevant subject. Therefore, a small library of chromone derivatives was synthesized and screened toward human monoamine oxidase isoforms (hMAO-A and hMAO-B). The structure-activity relationships studies strengthen the importance of the amide spacer and the direct linkage of carbonyl group to the γ-pyrone ring, along with the presence of meta and para substituents in the exocyclic ring. The most potent MAO-B inhibitors were N-(3'-chlorophenyl)-4-oxo-4H-chromene-3-carboxamide (20) (IC50 = 403 pM) and N-(3',4'-dimethylphenyl)-4-oxo-4H-chromene-3-carboxamide (27) (IC50 = 669 pM), acting as competitive and noncompetitive reversible inhibitors, respectively. Computational docking studies provided insights into enzyme-inhibitor interactions and a rationale for the observed selectivity and potency. Compound 27 stands out due to its favorable toxicological profile and physicochemical properties, which pointed toward blood-brain barrier permeability, thus being a valid candidate for subsequent animal studies.

Molecular inflation, attrition and the rule of five

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

Human microbial metabolites as a source of new drugs

Crosstalk between the microbiome and the human host is mediated by specific ligand-receptor interactions involving microbially generated metabolites that can be either agonists or antagonists of human proteins. The evolved co-compatibility of gut microbiota with human systems points to a potentially rich area for discovering new drug-like molecules that are both highly specific modulators of human pathways and derisked for adverse effects. In this review, we discuss the rapidly growing research into the role of microbial metabolites in human health and suggest potential strategies for developing these molecules into therapeutic agents.