Early Drug Discovery Research Articles

Fragment Pose Prediction Using Non-equilibrium Candidate Monte Carlo and Molecular Dynamics Simulations.

Part of early stage drug discovery involves determining how molecules may bind to the target protein. Through understanding where and how molecules bind, chemists can begin to build ideas on how to design improvements to increase binding affinities. In this retrospective study, we compare how computational approaches like docking, molecular dynamics (MD) simulations, and a non-equilibrium candidate Monte Carlo (NCMC)-based method (NCMC + MD) perform in predicting binding modes for a set of 12 fragment-like molecules, which bind to soluble epoxide hydrolase. We evaluate each method's effectiveness in identifying the dominant binding mode and finding additional binding modes (if any). Then, we compare our predicted binding modes to experimentally obtained X-ray crystal structures. We dock each of the 12 small molecules into the apo-protein crystal structure and then run simulations up to 1 μs each. Small and fragment-like molecules likely have smaller energy barriers separating different binding modes by virtue of relatively fewer and weaker interactions relative to drug-like molecules and thus likely undergo more rapid binding mode transitions. We expect, thus, to see more rapid transitions between binding modes in our study. Following this, we build Markov State Models to define our stable ligand binding modes. We investigate if adequate sampling of ligand binding modes and transitions between them can occur at the microsecond timescale using traditional MD or a hybrid NCMC+MD simulation approach. Our findings suggest that even with small fragment-like molecules, we fail to sample all the crystallographic binding modes using microsecond MD simulations, but using NCMC+MD, we have better success in sampling the crystal structure while obtaining the correct populations.

A Physiologically Based in Silico Tool to Assess the Risk of Drug-Related Crystalluria.

Drug precipitation in the nephrons of the kidney can cause drug-induced crystal nephropathy (DICN). To aid mitigation of this risk in early drug discovery, we developed a physiologically based in silico model to predict DICN in rats, dogs, and humans. At a minimum, the likelihood of DICN is determined by the level of systemic exposure to the molecule, the molecule's physicochemical properties and the unique physiology of the kidney. Accordingly, the proposed model accounts for these properties in order to predict drug exposure relative to solubility along the nephron. Key physiological parameters of the kidney were codified in a manner consistent with previous reports. Quantitative structure-activity relationship models and in vitro assays were used to estimate drug-specific physicochemical inputs to the model. The proposed model was calibrated against urinary excretion data for 42 drugs, and the utility for DICN prediction is demonstrated through application to 20 additional drugs.

Optimization of experimental conditions for skin-PAMPA measurements.

In recent years, the parallel artificial membrane permeability assay (PAMPA) has been extended for prediction of skin permeation by developing an artificial membrane which mimics the stratum corneum structure, skin-PAMPA. In the present work, the different parameters affecting skin-PAMPA permeability, such as incubation time and stirring, have been studied to establish ideal assay conditions to generate quality data for a screening of active pharmaceutical ingredients (API) in early stage drug discovery. Another important parameter is membrane retention, which shows dependence on lipophilicity when compounds are in their neutral form. Furthermore, the stability of the membrane has been investigated at different pH values, especially at basic pHs. Finally, a good correlation between human skin permeability and skin-PAMPA permeability, with a large dataset (n = 46), has been established. The optimized assay conditions were an incubation time of 4 hours with stirring in a pH below 8. With all these considerations the thickness of the aqueous boundary layer is decreased as much as possible and the membrane stability is guaranteed.

Drug Flux Across RPE Cell Models: The Hunt for An Appropriate Outer Blood-Retinal Barrier Model for Use in Early Drug Discovery.

The retinal pigment epithelial (RPE) cell monolayer forms the outer blood–retinal barrier and has a crucial role in ocular pharmacokinetics. Although several RPE cell models are available, there have been no systematic comparisons of their barrier properties with respect to drug permeability. We compared the barrier properties of several RPE secondary cell lines (ARPE19, ARPE19mel, and LEPI) and both primary (hfRPE) and stem-cell derived RPE (hESC-RPE) cells by investigating the permeability of nine drugs (aztreonam, ciprofloxacin, dexamethasone, fluconazole, ganciclovir, ketorolac, methotrexate, voriconazole, and quinidine) across cell monolayers. ARPE19, ARPE19mel, and hfRPE cells displayed a narrow Papp value range, with relatively high permeation rates (5.2–26 × 10−6 cm/s. In contrast, hESC-RPE and LEPI cells efficiently restricted the drug flux, and displayed even lower Papp values than those reported for bovine RPE-choroid, with the range of 0.4–32 cm−6/s (hESC-RPE cells) and 0.4–29 × 10−6 cm/s, (LEPI cells). Therefore, ARPE19, ARPE19mel, and hfRPE cells failed to form a tight barrier, whereas hESC-RPE and LEPI cells restricted the drug flux to a similar extent as bovine RPE-choroid. Therefore, LEPI and hESC-RPE cells are valuable tools in ocular drug discovery.

In silico Strategies to Support Fragment-to-Lead Optimization in Drug Discovery.

Fragment-based drug (or lead) discovery (FBDD or FBLD) has developed in the last two decades to become a successful key technology in the pharmaceutical industry for early stage drug discovery and development. The FBDD strategy consists of screening low molecular weight compounds against macromolecular targets (usually proteins) of clinical relevance. These small molecular fragments can bind at one or more sites on the target and act as starting points for the development of lead compounds. In developing the fragments attractive features that can translate into compounds with favorable physical, pharmacokinetics and toxicity (ADMET—absorption, distribution, metabolism, excretion, and toxicity) properties can be integrated. Structure-enabled fragment screening campaigns use a combination of screening by a range of biophysical techniques, such as differential scanning fluorimetry, surface plasmon resonance, and thermophoresis, followed by structural characterization of fragment binding using NMR or X-ray crystallography. Structural characterization is also used in subsequent analysis for growing fragments of selected screening hits. The latest iteration of the FBDD workflow employs a high-throughput methodology of massively parallel screening by X-ray crystallography of individually soaked fragments. In this review we will outline the FBDD strategies and explore a variety of in silico approaches to support the follow-up fragment-to-lead optimization of either: growing, linking, and merging. These fragment expansion strategies include hot spot analysis, druggability prediction, SAR (structure-activity relationships) by catalog methods, application of machine learning/deep learning models for virtual screening and several de novo design methods for proposing synthesizable new compounds. Finally, we will highlight recent case studies in fragment-based drug discovery where in silico methods have successfully contributed to the development of lead compounds.

The Kinetic Component in Drug Discovery: Using the Most Basic Pharmacological Concepts to Advance in Selecting Drugs to Combat CNS Diseases.

To reach the central nervous system (CNS), drugs must cross the brain-blood barrier and have appropriate pharmacokinetic/dynamic properties. However, in early drug discovery steps, the selection of lead compounds, for example, those targeting G-protein-coupled receptors (GPCRs), is made according to i) affinity, which is calculated in in vitro equilibrium conditions, and ii) potency, a signal transduction-related parameter, usually quantified at a fixed time-point in a heterologous expression system. This paper argues that kinetics must be considered in the early steps of lead compound selection. While affinity calculation requires the establishment of a ligand-receptor equilibrium, the signal transduction starts as soon as the receptor senses the agonist. Taking cAMP production as an example, the in vitro-measured cytoplasmic levels of this cyclic nucleotide do not depend on equilibrium dissociation constant, KD. Signaling occurs far from the equilibrium and correlates more with the binding rate (kon) than with KD. Furthermore, residence time, a parameter to consider in lead optimization, may significantly vary from in vitro to in vivo conditions. The results are discussed from the perspective of dopaminergic neurotransmission and dopamine-receptor-based drug discovery.

Flexibility in early drug discovery: focus on the beyond-Rule-of-5 chemical space

Large and flexible compounds are of interest in pharmaceutical programs aimed at challenging protein targets that cannot be modulated by Rule of Five (Ro5)-compliant small molecules. Given their particular structural features, early drug discovery is now in charge of identifying which molecular descriptors should be used in the often called beyond-Rule-of-5 (bRo5) chemical space. Here, we focus on flexibility descriptors. First, we discuss the concept of flexibility and then focus on the number of rotatable bonds (NRot), the most common in silico descriptor. After identifying the pros and cons of NRot, we discuss how Kier's index Φ can replace NRot, and the limits of 3D descriptors. Finally, we show how a misuse of NRot and Φ can result in incorrect interpretations of the impact of flexibility in the bRo5 space and how flexibility has potential in the prospective design of orally bioavailable bRo5 drug candidates.

New Chemical Modalities and Strategic Thinking in Early Drug Discovery.

The classical toolbox for drug discovery is continuously expanding beyond traditional small molecules. New chemical modalities including RNA therapeutics, protein degraders, cyclopeptides, antibody drug conjugates, and gene therapy have matured, demonstrating clinical success and are now considered early in target appraisal. In this Viewpoint, we highlight recent progress in the field.

In silico analysis of alternative splicing on drug-target gene interactions

Identifying and evaluating the right target are the most important factors in early drug discovery phase. Most studies focus on one protein ignoring the multiple splice-variant or protein-isoforms, which might contribute to unexpected therapeutic activity or adverse side effects. Here, we present computational analysis of cancer drug-target interactions affected by alternative splicing. By integrating information from publicly available databases, we curated 883 FDA approved or investigational stage small molecule cancer drugs that target 1,434 different genes, with an average of 5.22 protein isoforms per gene. Of these, 618 genes have ≥5 annotated protein-isoforms. By analyzing the interactions with binding pocket information, we found that 76% of drugs either miss a potential target isoform or target other isoforms with varied expression in multiple normal tissues. We present sequence and structure level alignments at isoform-level and make this information publicly available for all the curated drugs. Structure-level analysis showed ligand binding pocket architectures differences in size, shape and electrostatic parameters between isoforms. Our results emphasize how potentially important isoform-level interactions could be missed by solely focusing on the canonical isoform, and suggest that on- and off-target effects at isoform-level should be investigated to enhance the productivity of drug-discovery research.

Freely Accessible Chemical Database Resources of Compounds for In Silico Drug Discovery.

In silico drug discovery has been proved to be a solidly established key component in early drug discovery. However, this task is hampered by the limitation of quantity and quality of compound databases for screening. In order to overcome these obstacles, freely accessible database resources of compounds have bloomed in recent years. Nevertheless, how to choose appropriate tools to treat these freely accessible databases is crucial. To the best of our knowledge, this is the first systematic review on this issue. The existed advantages and drawbacks of chemical databases were analyzed and summarized based on the collected six categories of freely accessible chemical databases from literature in this review. Suggestions on how and in which conditions the usage of these databases could be reasonable were provided. Tools and procedures for building 3D structure chemical libraries were also introduced. In this review, we described the freely accessible chemical database resources for in silico drug discovery. In particular, the chemical information for building chemical database appears as attractive resources for drug design to alleviate experimental pressure.

Embedding of Precision-Cut Lung Slices in Engineered Hydrogel Biomaterials Supports Extended Ex Vivo Culture.

Maintaining the three-dimensional architecture and cellular complexity of lung tissue ex vivo can enable elucidation of the cellular and molecular pathways underlying chronic pulmonary diseases. Precision-cut lung slices (PCLS) are one human-lung model with the potential to support critical mechanistic studies and early drug discovery. However, many studies report short culture times of 7-10 days. Here, we systematically evaluated poly(ethylene glycol)-based hydrogel platforms for the encapsulation of PCLS. We demonstrated the ability to support ex vivo culture of embedded PCLS for at least 21 days compared with control PCLS floating in media. These customized hydrogels maintained PCLS architecture (no difference), viability (4.7-fold increase, P < 0.0001), and cellular phenotype as measured by SFTPC (1.8-fold increase, P < 0.0001) and vimentin expression (no change) compared with nonencapsulated controls. Collectively, these results demonstrate that hydrogel biomaterials support the extended culture times required to study chronic pulmonary diseases ex vivo using PCLS technology.

Rolling circle amplification-driven encoding of different fluorescent molecules for simultaneous detection of multiple DNA repair enzymes at the single-molecule level.

DNA repair enzymes (e.g., DNA glycosylases) play a critical role in the repair of DNA lesions, and their aberrant levels are associated with various diseases. Herein, we develop a sensitive method for simultaneous detection of multiple DNA repair enzymes based on the integration of single-molecule detection with rolling circle amplification (RCA)-driven encoding of different fluorescent molecules. We use human alkyladenine DNA glycosylase (hAAG) and uracil DNA glycosylase (UDG) as the target analytes. We design a bifunctional double-stranded DNA (dsDNA) substrate with a hypoxanthine base (I) in one strand for hAAG recognition and an uracil (U) base in the other strand for UDG recognition, whose cleavage by APE1 generates two corresponding primers. The resultant two primers can hybridize with their respective circular templates to initiate RCA, resulting in the incorporation of multiple Cy3-dCTP and Cy5-dGTP nucleotides into the amplified products. After magnetic separation and exonuclease cleavage, the Cy3 and Cy5 fluorescent molecules in the amplified products are released into the solution and subsequently quantified by total internal reflection fluorescence (TIRF)-based single-molecule detection, with Cy3 indicating the presence of hAAG and Cy5 indicating the presence of UDG. This strategy greatly increases the number of fluorescent molecules per concatemer through the introduction of RCA-driven encoding of different fluorescent molecules, without the requirement of any specially labeled detection probes for simultaneous detection. Due to the high amplification efficiency of RCA and the high signal-to-ratio of single-molecule detection, this method can achieve a detection limit of 6.10 × 10-9 U mL-1 for hAAG and 1.54 × 10-9 U mL-1 for UDG. It can be further applied for simultaneous detection of multiple DNA glycosylases in cancer cells at the single-cell level and the screening of DNA glycosylase inhibitors, holding great potential in early clinical diagnosis and drug discovery.

Pan-transcriptome-based candidate therapeutic discovery for idiopathic pulmonary fibrosis.

Background:There are two US Food and Drug Administration (FDA)-approved drugs, pirfenidone and nintedanib, for treatment of patients with idiopathic pulmonary fibrosis (IPF). However, neither of these drugs provide a cure. In addition, both are associated with several drug-related adverse events. Hence, the pursuit for newer IPF therapeutics continues. Recent studies show that joint analysis of systems-biology-level information with drug–disease connectivity are effective in discovery of biologically relevant candidate therapeutics.Methods:Publicly available gene expression signatures from patients with IPF were used to query a large-scale perturbagen signature library to discover compounds that can potentially reverse dysregulated gene expression in IPF. Two methods were used to calculate IPF–compound connectivity: gene expression-based connectivity and feature-based connectivity. Identified compounds were further prioritized if their shared mechanism(s) of action were IPF-related.Results:We found 77 compounds as potential candidate therapeutics for IPF. Of these, 39 compounds are either FDA-approved for other diseases or are currently in phase II/III clinical trials suggesting their repurposing potential for IPF. Among these compounds are multiple receptor kinase inhibitors (e.g. nintedanib, currently approved for IPF, and sunitinib), aurora kinase inhibitor (barasertib), epidermal growth factor receptor inhibitors (erlotinib, gefitinib), calcium channel blocker (verapamil), phosphodiesterase inhibitors (roflumilast, sildenafil), PPAR agonists (pioglitazone), histone deacetylase inhibitors (entinostat), and opioid receptor antagonists (nalbuphine). As a proof of concept, we performed in vitro validations with verapamil using lung fibroblasts from IPF and show its potential benefits in pulmonary fibrosis.Conclusions:As about half of the candidates discovered in this study are either FDA-approved or are currently in clinical trials for other diseases, rapid translation of these compounds as potential IPF therapeutics is possible. Further, the integrative connectivity analysis framework in this study can be adapted in early phase drug discovery for other common and rare diseases with transcriptomic profiles.The reviews of this paper are available via the supplemental material section.

In Vitro Image-Based Assay for Trypanosoma cruzi Intracellular Forms.

The advances in development and popularization of automated fluorescence microscopes and pipetting robots allowed scientists to establish high-throughput compound screening using image-based assays for Trypanosoma cruzi intracellular forms, which are associated to chronic Chagas disease. An intracellular T. cruzi image-based assay is a valuable tool to early stage drug discovery for Chagas disease, because it allows scientists to assess a compound's efficacy and safety in the same experiment. During the last 10 years, several improvements have been incorporated into intracellular T. cruzi assay protocols to make them more predictable in what happens with parasites within an infected organism. In the present chapter, a protocol will be presented for an intracellular T. cruzi assay, but at a low-throughput scale, more compatible with facilities in many academic laboratories.

Demonstration of the utility of DOS-derived fragment libraries for rapid hit derivatisation in a multidirectional fashion.

Organic synthesis underpins the evolution of weak fragment hits into potent lead compounds. Deficiencies within current screening collections often result in the requirement of significant synthetic investment to enable multidirectional fragment growth, limiting the efficiency of the hit evolution process. Diversity-oriented synthesis (DOS)-derived fragment libraries are constructed in an efficient and modular fashion and thus are well-suited to address this challenge. To demonstrate the effective nature of such libraries within fragment-based drug discovery, we herein describe the screening of a 40-member DOS library against three functionally distinct biological targets using X-Ray crystallography. Firstly, we demonstrate the importance for diversity in aiding hit identification with four fragment binders resulting from these efforts. Moreover, we also exemplify the ability to readily access a library of analogues from cheap commercially available materials, which ultimately enabled the exploration of a minimum of four synthetic vectors from each molecule. In total, 10–14 analogues of each hit were rapidly accessed in three to six synthetic steps. Thus, we showcase how DOS-derived fragment libraries enable efficient hit derivatisation and can be utilised to remove the synthetic limitations encountered in early stage fragment-based drug discovery.

Optimized Inhibitors of MDM2 via an Attempted Protein-Templated Reductive Amination.

Innovative and efficient hit‐identification techniques are required to accelerate drug discovery. Protein‐templated fragment ligations represent a promising strategy in early drug discovery, enabling the target to assemble and select its binders from a pool of building blocks. Development of new protein‐templated reactions to access a larger structural diversity and expansion of the variety of targets to demonstrate the scope of the technique are of prime interest for medicinal chemists. Herein, we present our attempts to use a protein‐templated reductive amination to target protein‐protein interactions (PPIs), a challenging class of drug targets. We address a flexible pocket, which is difficult to achieve by structure‐based drug design. After careful analysis we did not find one of the possible products in the kinetic target‐guided synthesis (KTGS) approach, however subsequent synthesis and biochemical evaluation of each library member demonstrated that all the obtained molecules inhibit MDM2. The most potent library member (K i=0.095 μm) identified is almost as active as Nutlin‐3, a potent inhibitor of the p53‐MDM2 PPI.

Biophysical methods in early drug discovery.

Biophysical methods such as mass spectrometry, surface plasmon resonance, nuclear magnetic resonance, and both differential scanning isothermal titration calorimetry are now well established as key components of the early drug discovery process. These approaches are used successfully for a range of activities, including assay development, primary screening, hit confirmation and detailed mechanistic characterisation of compound binding. Matching the speed, sensitivity and information content of the various techniques to the generation of critical data and information required at each phase of the drug discovery process has been key. This review describes the framework by which these methods have been applied in the drug discovery process and provides case studies to exemplify the impact.

Cyclodextrin Inclusion of Medicinal Compounds for Enhancement of their Physicochemical and Biopharmaceutical Properties.

Owing to their wide structural diversity and unique complexing properties, cyclodextrins (CDs) find manifold applications in drug discovery and development. The focus of this mini-review is on their uses as 'enabling excipients' both in the context of early drug discovery and in subsequent optimisation of drug performance. Features highlighted here include descriptions of the structures of CDs, synthetic derivatisation to fine-tune their properties, the nature of inclusion complexation of drugs within the CD cavity, methodology for the study of free and complexed hosts in the solid state and in solution, the inherent pharmacological activity of several CDs and its utility, novel CD-based drug delivery systems, and the role of CDs in drug discovery and optimisation. Illustrative examples are generally based on research reported during the last two decades. Application of CDs to the optimisation of the performance of established drugs is commonplace, but there are many opportunities for the intervention of CDs during the early stages of drug discovery, which could guide the selection of suitable candidates for development, thereby contributing to reducing the attrition rate of new molecular entities.

A novel evaluation method for determining drug-induced hepatotoxicity using 3D bio-printed human liver tissue

Predicting drug-induced liver injury is important in early stage drug discovery; however, an accurate prediction with existing hepatotoxicity evaluation tools is difficult. Conventional monolayer (2D) cultures have short viabilities and are therefore inappropriate for performing long-term toxicity tests. Conventionally used 200-μm spheroids also have toxicity detection limits. The goal of this study was to develop a humanized liver tissue capable of evaluating long-term toxicity with high sensitivity. Spheroids consisting of co-cultured cryopreserved primary human hepatocytes and human hepatic stellate cells were developed using a 3D bio-printer. The “3D bio-printed liver tissue”, of ∼1 mm, was then used for long-term viability assessments (over 25 days) based on ATP, albumin, and urea levels. Hepatotoxicity evaluation was performed by analyzing the expression of genes involved in drug metabolism and transport over a 2-week drug exposure period. The 3D bio-printed liver tissue showed improved viability and enhanced gene expression of enzymes related to drug metabolism and transport, as compared to the controls. Additionally, the 3D bio-printed liver tissue demonstrated a high sensitivity for hepatotoxicity evaluation when combined with pathological evaluation and measurements for ATP production, and secretion of albumin and urea. In conclusion, the 3D bio-printed liver tissue was able to detect the toxicity of compounds that was, otherwise, undetected by 2D culture and conventionally used spheroids. These findings demonstrate a 3D bio-printed liver tissue with increased accuracy of hepatotoxicity prediction in the early stages of drug discovery, as compared to currently available methods.

Small Polar Hits against S. aureus: Screening, Initial Hit Optimization, and Metabolomic Studies.

The global prevalence of antibacterial resistance requires new antibacterial drugs with novel chemical scaffolds and modes of action. It is also vital to design compounds with optimal physicochemical properties to permeate the bacterial cell envelope. We described an approach of combining and integrating whole cell screening and metabolomics into early antibacterial drug discovery using a library of small polar compounds. Whole cell screening of a diverse library of small polar compounds against Staphylococcus aureus gave compound 2. Hit expansion was carried out to determine structure–activity relationships. A selection of compounds from this series, together with other screened active compounds, was subjected to an initial metabolomics study to provide a metabolic fingerprint of the mode of action. It was found that compound 2 and its analogues have a different mode of action from some of the known antibacterial compounds tested. This early study highlighted the potential of whole cell screening and metabolomics in early antibacterial drug discovery. Future works will require improving potency and performing orthogonal studies to confirm the modes of action.