Hit-to-Lead: Hit Validation and Assessment

A new computational cross-structure-activity relationship (C-SAR) approach applies to a selective HDAC6 inhibitor dataset for accelerated structure development

Several structure-activity relationship (SAR) methodologies have been developed for the research community to improve the potential activity of prototype structures. To accomplish this, Topliss proposed the Topliss tree and the Topliss Batchwise scheme for structure development. Structure development necessitates tactics beyond traditional SAR procedures when handling issues, such as rapid structural inactivation during development. SAR data is vital for altering chemical structures and addressing compound problems. Obtaining unique SAR data that provides strategic options for structure transformation relevant to every chemotype and not limited to a specific parent structure, as the Topliss approach does, is challenging. In this context, we present the C-SAR strategy, which addresses these issues and accelerates structural development. The C-SAR method provides insights into converting an inactive compound into an active one. We used cheminformatics and molecular docking tools to study a chemical library of diverse chemotypes targeting HDAC6, arranging it in matched molecular pairs (MMPs) with high structural activity landscape index (SALI) values of 820880 and a diversity index of 0.5827 and identifying C-SAR highlights based on repetitive pharmacophoric substitution patterns across different MMP chemotypes that resulted in activity cliffs. C-SAR is beneficial for SAR expansion when high-quality structural data are available to study a dataset of various MMPs from a specific class of compounds and allows using the obtained C-SAR highlights to design compounds of novel chemotypes beyond the investigated dataset. Data imputation using deep-learning predictive models may address the issue of data availability for C-SAR.

Higher Contrast in 1H-Observed NMR Ligand Screening with the PEARLScreen Experiment

For initial hit identification in fragment-based drug design, NMR is one of the preferred methods, with the well-established ligand screening experiments T1ρ, waterLOGSY and STD. Here, we present a new, perfect echo-based experiment with superior binding sensitivity, the PEARLScreen. The sensitivity enhancement is based on longer relaxation delays as well as active exchange broadening, enabled by the perfect echo scheme. This allowed a reduction of the protein concentration by up to one order of magnitude in comparison to the conventional experiments on the same screening setup without losing binding sensitivity. The experiment was tested throughout the whole field range of commercially available spectrometers from 80 to 1200 MHz, where at the highest field even a reduction of the required protein amount by a factor of 40 is within reach, thanks to larger compound mixtures and enhanced exchange effects. Based on the superior performance already on standard NMR setups, we expect the PEARLScreen to become the standard screening experiment for 1H-detected ligand screening.

A Review on Perception of Binding Kinetics in Affinity Biosensors: Challenges and Opportunities

There are challenges associated with design and development of affinity biosensors due to the complicated multiphysics nature of the system. Understanding the binding interaction between target molecules and immobilized receptors and its kinetics is a crucial step to develop robust and reliable biosensor technologies. Evaluation of binding kinetics in biosensors becomes more important and challenging for clinical samples with a complex matrix. Despite drastic advancements in biosensor technologies, having a practical perception of the binding kinetics has remained a critical bottleneck due to limited fundamental understanding. This Review aims to provide a comprehensive discussion on concepts and advances developed so far for the perception of binding kinetics in affinity biosensors. Here, modeling approaches and measurement techniques are presented to characterize the binding interactions in biosensor technologies, while the effect of fouling and secondary factors in the binding interactions will be discussed in the concept of kinetics. This Review will investigate the existing research gaps and potential opportunities in the perception of binding kinetics and challenges to develop robust and reliable biosensors.

Machine Learning Assisted Hit Prioritization for High Throughput Screening in Drug Discovery

Efficient prioritization of bioactive compounds from high throughput screening campaigns is a fundamental challenge for accelerating drug development efforts. In this study, we present the first data-driven approach to simultaneously detect assay interferents and prioritize true bioactive compounds. By analyzing the learning dynamics during training of a gradient boosting model on noisy high throughput screening data using a novel formulation of sample influence, we are able to distinguish between compounds exhibiting the desired biological response and those producing assay artifacts. Therefore, our method enables false positive and true positive detection without relying on prior screens or assay interference mechanisms, making it applicable to any high throughput screening campaign. We demonstrate that our approach consistently excludes assay interferents with different mechanisms and prioritizes biologically relevant compounds more efficiently than all tested baselines, including a retrospective case study simulating its use in a real drug discovery campaign. Finally, our tool is extremely computationally efficient, requiring less than 30 s per assay on low-resource hardware. As such, our findings show that our method is an ideal addition to existing false positive detection tools and can be used to guide further pharmacological optimization after high throughput screening campaigns.

Optimizing drug discovery for snakebite envenoming via a high-throughput phospholipase A2 screening platform

Snakebite envenoming is a neglected tropical disease that causes as many as 1.8 million envenomings and 140,000 deaths annually. To address treatment limitations that exist with current antivenoms, the search for small molecule drug-based inhibitors that can be administered as early interventions has recently gained traction. Snake venoms are complex mixtures of proteins, peptides and small molecules and their composition varies substantially between and within snake species. The phospholipases A2 (PLA2) are one of the main pathogenic toxin classes found in medically important viper and elapid snake venoms, yet varespladib, a drug originally developed for the treatment of acute coronary syndrome, remains the only PLA2 inhibitor shown to effectively neutralise venom toxicity in vitro and in vivo, resulting in an extremely limited drug portfolio. Here, we describe a high-throughput drug screen to identify novel PLA2 inhibitors for repurposing as snakebite treatments. We present method optimisation of a 384-well plate, colorimetric, high-throughput screening assay that allowed for a throughput of ∼2,800 drugs per day, and report on the screening of a ∼3,500 post-phase I repurposed drug library against the venom of the Russell's viper, Daboia russelii. We further explore the broad-spectrum inhibitory potential and efficacy of the resulting top hits against a range of medically important snake venoms and demonstrate the utility of our method in determining drug EC50s. Collectively, our findings support the future application of this method to fully explore the chemical space to discover novel PLA2-inhibiting drugs of value for preventing severe pathology caused by snakebite envenoming.

Machine-Learning- and Structure-Based Virtual Screening for Selecting Cinnamic Acid Derivatives as Leishmania major DHFR-TS Inhibitors

The critical enzyme dihydrofolate reductase-thymidylate synthase in Leishmania major (LmDHFR-TS) serves a dual-purpose role and is essential for DNA synthesis, a cornerstone of the parasite's reproductive processes. Consequently, the development of inhibitors against LmDHFR-TS is crucial for the creation of novel anti-Leishmania chemotherapies. In this study, we employed an in-house database containing 314 secondary metabolites derived from cinnamic acid that occurred in the Asteraceae family. We conducted a combined ligand/structure-based virtual screening to identify potential inhibitors against LmDHFR-TS. Through consensus analysis of both approaches, we identified three compounds, i.e., lithospermic acid (237), diarctigenin (306), and isolappaol A (308), that exhibited a high probability of being inhibitors according to both approaches and were consequently classified as promising hits. Subsequently, we expanded the binding mode examination of these compounds within the active site of the test enzyme through molecular dynamics simulations, revealing a high degree of structural stability and minimal fluctuations in its tertiary structure. The in silico predictions were then validated through in vitro assays to examine the inhibitory capacity of the top-ranked naturally occurring compounds against LmDHFR-TS recombinant protein. The test compounds effectively inhibited the enzyme with IC50 values ranging from 6.1 to 10.1 μM. In contrast, other common cinnamic acid derivatives (i.e., flavonoid glycosides) from the Asteraceae family, such as hesperidin, isovitexin 4'-O-glucoside, and rutin, exhibited low activity against this target. The selective index (SI) for all tested compounds was determined using HsDHFR with moderate inhibitory effect. Among these hits, lignans 306 and 308 demonstrated the highest selectivity, displaying superior SI values compared to methotrexate, the reference inhibitor of DHFR-TS. Therefore, continued research into the anti-leishmanial potential of these C6C3-hybrid butyrolactone lignans may offer a brighter outlook for combating this neglected tropical disease.

Docking and Molecular Dynamics Identify Leads against 5 Alpha Reductase 2 for Benign Prostate Hyperplasia Treatment

Steroid 5 alpha-reductase 2 (5αR-2) is a membrane-embedded protein that together with other isoforms plays a key role in the metabolism of steroids. This enzyme catalyzes the reduction of testosterone to the more potent ligand, dihydrotestosterone (DHT) in the prostate. Androgens, testosterone, and DHT play important roles in prostate growth, development, and function. At the same time, both testosterone and DHT have been implicated in the pathogenesis of benign prostate hyperplasia (BPH). Inhibition of the DHT formation, therefore, provides a therapeutic strategy that offers the possibility of preventing, delaying, or treating BPH. Currently, two steroidal drugs that inhibit 5αR-2, dutasteride and finasteride, have been approved for clinical use. These two come at a high cost and also portray undesirable sexual side effects which necessitate the need to find new chemotherapeutic alternatives for the disease. Based on the aforementioned, finasteride and dutasteride were subjected to scaffold hopping, fragment-based de novo design, molecular docking, and molecular dynamics simulations employing databases like ChEMBL, DrugBank, PubChem, ChemSpider, and Zinc15 in the identification of potential hits targeting 5αR-2. Altogether, ten novel compounds targeting 5αR-2 were identified with binding energies lower or comparable to finasteride and dutasteride, the main inhibitors for this target. Molecular docking and molecular dynamics simulations studies identify amino acid residues Glu57, Phe219, Phe223, and Leu224 to be critical for ligand binding and complex stability. The physicochemical and pharmacological profiling suggests the potential of the hit compounds to be drug-like and orally active. Similarly, the quality parameter assessments revealed the hits possess LELP greater than 3 implying their promise as lead-like molecules. The compounds A5, A9, and A10 were, respectively, predicted to treat prostate disorders with Pa (0.188, 0.361, and 0.270) and Pi (0.176, 0.050, and 0.093), while A8 and A9 were found to be associated with BPH treatment with Pa (0.09 and 0.127) and Pi (0.077 and 0.033), respectively. Structural similarity searches via DrugBank identified the drugs faropenem, acemetacin, estradiol valerate, and yohimbine to be useful for BPH treatment suggesting the de novo designed ligands as potential chemotherapeutic agents for treating this disease.

Pathogen-associated gene discovery workflows for novel antivirulence therapeutic development

Novel therapeutics to manage bacterial infections are urgently needed as the impact and prevalence of antimicrobial resistance (AMR) grows. Antivirulence therapeutics are an alternative approach to antibiotics that aim to attenuate virulence rather than target bacterial essential functions, while minimizing microbiota perturbation and the risk of AMR development. Beyond known virulence factors, pathogen-associated genes (PAGs; genes found only in pathogens to date) may play an important role in virulence or host association. Many identified PAGs encode uncharacterized hypothetical proteins and represent an untapped wealth of novel drug targets. Here, we review current advances in antivirulence drug research and development, including PAG identification, and provide a comprehensive workflow from the discovery of antivirulence drug targets to drug discovery. We highlight the importance of integrating bioinformatic/genomic-based methods for novel virulence factor discovery, coupled with experimental characterization, into existing drug screening platforms to develop novel and effective antivirulence drugs.

Angiotensin II receptor blockers (ARBs) and manufacturing contamination: A retrospective National Register Study into suspected associated adverse drug reactions

AIMS

The aim of this study was to determine if any suspected adverse drug reactions (ADRs) observed with the use of angiotensin II receptor blockers (ARBs) could be linked to either (a) their unique respective physicochemical and pharmacological profiles and (b) the recently disclosed suspected carcinogenic manufacturing contaminants found in certain sartan drug class batches.

METHODS

The pharmacology profiles of ARBs were data-mined from the Chemical Database of bioactive molecules with drug-like properties, European Molecular Biology Laboratory (ChEMBL). Suspected ADR data (from 01/2016-10/2022, inclusive) and prescribing rates of ARBs over a 5-year prescribing window (from 09/2016 to 08/2021, inclusive) were obtained via analysis of the United Kingdom Medicines and Healthcare products Regulatory Authority (MHRA) Yellow Card drug analysis profile and Open prescribing databases, respectively.

RESULTS

The overall suspected ADRs and fatalities per 100 000 prescriptions identified across the ARBs studied were found to be different between the sartan drug class members (chi-squared test, P < .05). There is a greater relative rate of reports for valsartan across all investigated organ classes of ADRs, than other ARBs, despite valsartan's more limited pharmacological profile and similar physicochemical properties to other sartans. The disparity in ADR reporting rates with valsartan vs other ARBs could be due to the dissimilarity in formulation excipients, patient factors and publicity surrounding batch contaminations, amongst others. Cancer-related ADRs and fatalities per 100 000 prescriptions identified across the ARBs studied are not statistically significant (chi-squared test, P > .05) based on the datasets used over the 5-year period.

CONCLUSION

No connection between ARB pharmacology and their suspected ADRs could be found. No conclusion between sartan batch contaminations and increased suspected cancer-related ADRs was found.

A Liquid Chromatography-Mass Spectrometry Method to Study the Interaction between Membrane Proteins and Low-Molecular-Weight Compound Mixtures

Molecular interaction analysis is an essential technique for the study of biomolecular functions and the development of new drugs. Most current methods generally require manipulation to immobilize or label molecules, and require advance identification of at least one of the two molecules in the reaction. In this study, we succeeded in detecting the interaction of low-molecular-weight (LMW) compounds with a membrane protein mixture derived from cultured cells expressing target membrane proteins by using the size exclusion chromatography-mass spectrometry (SEC-MS) method under the condition of 0.001% lauryl maltose neopentyl glycol as detergent and atmospheric pressure chemical ionization. This method allowed us to analyze the interaction of a mixture of medicinal herbal ingredients with a mixture of membrane proteins to identify the two interacting ingredients. As it does not require specialized equipment (e.g., a two-dimensional liquid chromatography system), this SEC-MS method enables the analysis of interactions between LMW compounds and relatively high-expressed membrane proteins without immobilization or derivatization of the molecules.

Homology Modeling, de Novo Design of Ligands, and Molecular Docking Identify Potential Inhibitors of Leishmania donovani 24-Sterol Methyltransferase

The therapeutic challenges pertaining to leishmaniasis due to reported chemoresistance and toxicity necessitate the need to explore novel pathways to identify plausible inhibitory molecules. Leishmania donovani 24-sterol methyltransferase (LdSMT) is vital for the synthesis of ergosterols, the main constituents of Leishmania cellular membranes. So far, mammals have not been shown to possess SMT or ergosterols, making the pathway a prime candidate for drug discovery. The structural model of LdSMT was elucidated using homology modeling to identify potential novel 24-SMT inhibitors via virtual screening, scaffold hopping, and de-novo fragment-based design. Altogether, six potential novel inhibitors were identified with binding energies ranging from −7.0 to −8.4 kcal/mol with e-LEA3D using 22,26-azasterol and S1–S4 obtained from scaffold hopping via the ChEMBL, DrugBank, PubChem, ChemSpider, and ZINC15 databases. These ligands showed comparable binding energy to 22,26-azasterol (−7.6 kcal/mol), the main inhibitor of LdSMT. Moreover, all the compounds had plausible ligand efficiency-dependent lipophilicity (LELP) scores above 3. The binding mechanism identified Tyr92 to be critical for binding, and this was corroborated via molecular dynamics simulations and molecular mechanics Poisson–Boltzmann surface area (MM-PBSA) calculations. The ligand A1 was predicted to possess antileishmanial properties with a probability of activity (Pa) of 0.362 and a probability of inactivity (Pi) of 0.066, while A5 and A6 possessed dermatological properties with Pa values of 0.205 and 0.249 and Pi values of 0.162 and 0.120, respectively. Structural similarity search via DrugBank identified vabicaserin, daledalin, zanapezil, imipramine, and cefradine with antileishmanial properties suggesting that the de-novo compounds could be explored as potential antileishmanial agents.

Adsorption to the Surface of Hemozoin Crystals: Structure-Based Design and Synthesis of Amino-Phenoxazine β-Hematin Inhibitors

In silico adsorption of eight antimalarials that inhibit β-hematin (synthetic hemozoin) formation identified a primary binding site on the (001) face, which accommodates inhibitors via formation of predominantly π-π interactions. A good correlation (r2 =0.64, P=0.017) between adsorption energies and the logarithm of β-hematin inhibitory activity was found for this face. Of 53 monocyclic, bicyclic and tricyclic scaffolds, the latter yielded the most favorable adsorption energies. Five new amino-phenoxazine compounds were pursued as β-hematin inhibitors based on adsorption behaviour. The 2-substituted phenoxazines show good to moderate β-hematin inhibitory activity (<100 μM) and Plasmodium falciparum blood stage activity against the 3D7 strain. N1 ,N1 -diethyl-N4 -(10H-phenoxazin-2-yl)pentane-1,4-diamine (P2a) is the most promising hit with IC50 values of 4.7±0.6 and 0.64±0.05 μM, respectively. Adsorption energies are predictive of β-hematin inhibitory activity, and thus the in silico approach is a beneficial tool for structure-based development of new non-quinoline inhibitors.

Synergy between machine learning and natural products cheminformatics: Application to the lead discovery of anthraquinone derivatives

Cheminformatics utilizing machine learning (ML) techniques have opened up a new horizon in drug discovery. This is owing to vast chemical space expansion with rocketing numbers of expected hits and lead compounds that match druggable macromolecular targets, in particular from natural compounds. Due to the natural products' (NP) structural complexity, uniqueness, and diversity, they could occupy a bigger space in pharmaceuticals, allowing the industry to pursue more selective leads in the nanomolar range of binding affinity. ML is an essential part of each step of the drug design pipeline, such as target prediction, compound library preparation, and lead optimization. Notably, molecular mechanic and dynamic simulations, induced docking, and free energy perturbations are essential in predicting best binding poses, binding free energy values, and molecular mechanics force fields. Those applications have leveraged from artificial intelligence (AI), which decreases the computational costs required for such costly simulations. This review aimed to describe chemical space and compound libraries related to NPs. High-throughput screening utilized for fractionating NPs and high-throughput virtual screening and their strategies, and significance, are reviewed. Particular emphasis was given to AI approaches, ML tools, algorithms, and techniques, especially in drug discovery of macrocyclic compounds and approaches in computer-aided and ML-based drug discovery. Anthraquinone derivatives were discussed as a source of new lead compounds that can be developed using ML tools for diverse medicinal uses such as cancer, infectious diseases, and metabolic disorders. Furthermore, the power of principal component analysis in understanding relevant protein conformations, and molecular modeling of protein-ligand interaction were also presented. Apart from being a concise reference for cheminformatics, this review is a useful text to understand the application of ML-based algorithms to molecular dynamics simulation and in silico absorption, distribution, metabolism, excretion, and toxicity prediction.

Evaluation of intermolecular interactions required for thermostability of a recombinant adenovirus within a film matrix

Thermostability of vaccines and biologic drugs are key to increasing global access to a variety of life-saving agents. In this report, we characterize interactions between a novel zwitterionic surfactant and adenovirus serotype 5 which allow the virus to remain stable at room temperature in a thin film matrix. Complexity of the adenovirus capsid and the polydispersity of the surfactant required use of a variety of techniques to achieve this goal. The CMC of the surfactant in Tris buffer (pH 6.5) was estimated to be 0.7-1.17 × 10^-4 M by the pyrene 1:3 ratio method. TEM images depict micelle formation around virus capsids. An estimated Kd of the virus-surfactant interaction of 2.25 × 10^-9 M was determined by isothermal titration calorimetry. Associated data suggest that this interaction may be thermodynamically favorable and entropically driven. A competitive saturation study and TEM images indicate that the surfactant also binds to hexon proteins on the virus capsid. Taken together, these data support the working hypothesis that the surfactant is capable of forming micelles in the solid and liquid state and that it forms a protective coating around the virus by binding to hexon proteins on the virus capsid during the film forming process.

Applications of isothermal titration calorimetry in pure and applied research from 2016 to 2020

The last 5 years have seen a series of advances in the application of isothermal titration microcalorimetry (ITC) and interpretation of ITC data. ITC has played an invaluable role in understanding multiprotein complex formation including proteolysis-targeting chimeras (PROTACS), and mitochondrial autophagy receptor Nix interaction with LC3 and GABARAP. It has also helped elucidate complex allosteric communication in protein complexes like trp RNA-binding attenuation protein (TRAP) complex. Advances in kinetics analysis have enabled the calculation of kinetic rate constants from pre-existing ITC data sets. Diverse strategies have also been developed to study enzyme kinetics and enzyme-inhibitor interactions. ITC has also been applied to study small molecule solvent and solute interactions involved in extraction, separation, and purification applications including liquid-liquid separation and extractive distillation. Diverse applications of ITC have been developed from the analysis of protein instability at different temperatures, determination of enzyme kinetics in suspensions of living cells to the adsorption of uremic toxins from aqueous streams.

The shaping of a molecular linguist: How a career studying DNA energetics revealed the language of molecular communication

My personal and professional journeys have been far from predictable based on my early childhood. Owing to a range of serendipitous influences, I miraculously transitioned from a rebellious, apathetic teenage street urchin who did poorly in school to a highly motivated, disciplined, and ambitious academic honors student. I was the proverbial “late bloomer.” Ultimately, I earned my PhD in biophysical chemistry at Yale, followed by a postdoc fellowship at Berkeley. These two meccas of thermodynamics, coupled with my deep fascination with biology, instilled in me a passion to pursue an academic career focused on mapping the energy landscapes of biological systems. I viewed differential energetics as the language of molecular communication that would dictate and control biological structures, as well as modulate the modes of action associated with biological functions. I wanted to be a “molecular linguist.” For the next 50 years, my group and I used a combination of spectroscopic and calorimetric techniques to characterize the energy profiles of the polymorphic conformational space of DNA molecules, their differential ligand-binding properties, and the energy landscapes associated with mutagenic DNA damage recognition, repair, and replication. As elaborated below, the resultant energy databases have enabled the development of quantitative molecular biology through the rational design of primers, probes, and arrays for diagnostic, therapeutic, and molecular-profiling protocols, which collectively have contributed to a myriad of biomedical assays. Such profiling is further justified by yielding unique energy-based insights that complement and expand elegant, structure-based understandings of biological processes.

N,N′-dialkyl-2-thiobarbituric acid based sulfonamides as potential SARS-CoV-2 main protease inhibitors

An efficient methodology was developed to generate novel N,N′-dialkyl-2-thiobarbituric acid based sulfonamides S1–S4 in good to excellent yields (84%–95%). The synthesized compounds S1–S4 were docked to screen their in silico activities against two enzymes i.e., SARS-CoV-2 main protease enzyme with unliganded active site (2019-nCoV, coronavirus disease 2019, COVID-19) PDB ID: 6Y84 and SARS-CoV-2 Mpro PDB ID: 6LU7. Furthermore, some in silico physicochemical and physicokinetic properties were evaluated using the OSIRIS Property Explorer, Molinspiration property calculator, ADMET property calculator, and GUSAR to assess these compounds as potential candidates as lead compounds for the quest of SARS-CoV-2 main protease inhibitors. Molecular docking analyses of the synthesized compounds predicted that compound S3 is more potent as SARS-CoV-2 main protease inhibitor with binding energy –11.65 kcal/mol in comparison with reference inhibitor N3 (–10.95 kcal/mol), whereas compounds S1, S2, and S4 recorded comparable binding energies –9.89, –10.84, and –10.94 kcal/mol with reference inhibitor N3, which were much better than remdesivir (–9.85 kcal/mol). In case of SARS-CoV-2 Mpro, all compounds S1–S4 with docking energy values of –7.28, –8.38, –8.31, and –7.34 kcal/mol, respectively, were found to be potent in comparison with reference inhibitor N3 (–6.31 kcal/mol) and remdesivir (–6.33 kcal/mol). Ligand efficiency values against the target SARS-CoV-2 proteins, as well as α-glucosidase and DNA-(apurinic or apyrimidinic site) lyase inhibition results of these newly synthesized compounds, were also found to be promising.

Searching Hit Potential Antimicrobials in Natural Compounds Space against Biofilm Formation

Biofilms are communities of microorganisms that can colonize biotic and abiotic surfaces and thus play a significant role in the persistence of bacterial infection and resistance to antimicrobial. About 65% and 80% of microbial and chronic infections are associated with biofilm formation, respectively. The increase in infections by multi-resistant bacteria instigates the need for the discovery of novel natural-based drugs that act as inhibitory molecules. The inhibition of diguanylate cyclases (DGCs), the enzyme implicated in the synthesis of the second messenger, cyclic diguanylate (c-di-GMP), involved in the biofilm formation, represents a potential approach for preventing the biofilm development. It has been extensively studied using PleD protein as a model of DGC for in silico studies as virtual screening and as a model for in vitro studies in biofilms formation. This study aimed to search for natural products capable of inhibiting the Caulobacter crescentus enzyme PleD. For this purpose, 224,205 molecules from the natural products ZINC15 database, have been evaluated through molecular docking and molecular dynamic simulation. Our results suggest trans-Aconitic acid (TAA) as a possible starting point for hit-to-lead methodologies to obtain new inhibitors of the PleD protein and hence blocking the biofilm formation.

NMR as a "Gold Standard" Method in Drug Design and Discovery

Studying disease models at the molecular level is vital for drug development in order to improve treatment and prevent a wide range of human pathologies. Microbial infections are still a major challenge because pathogens rapidly and continually evolve developing drug resistance. Cancer cells also change genetically, and current therapeutic techniques may be (or may become) ineffective in many cases. The pathology of many neurological diseases remains an enigma, and the exact etiology and underlying mechanisms are still largely unknown. Viral infections spread and develop much more quickly than does the corresponding research needed to prevent and combat these infections; the present and most relevant outbreak of SARS-CoV-2, which originated in Wuhan, China, illustrates the critical and immediate need to improve drug design and development techniques. Modern day drug discovery is a time-consuming, expensive process. Each new drug takes in excess of 10 years to develop and costs on average more than a billion US dollars. This demonstrates the need of a complete redesign or novel strategies. Nuclear Magnetic Resonance (NMR) has played a critical role in drug discovery ever since its introduction several decades ago. In just three decades, NMR has become a "gold standard" platform technology in medical and pharmacology studies. In this review, we present the major applications of NMR spectroscopy in medical drug discovery and development. The basic concepts, theories, and applications of the most commonly used NMR techniques are presented. We also summarize the advantages and limitations of the primary NMR methods in drug development.