Some Aspects and Convergence of Human and Veterinary Drug Repositioning

Drug innovation traditionally follows a de novo approach with new molecules through a complex preclinical and clinical pathway. In addition to this strategy, drug repositioning has also become an important complementary approach, which can be shorter, cheaper, and less risky. This review provides an overview of drug innovation in both human and veterinary medicine, with a focus on drug repositioning. The evolution of drug repositioning and the effectiveness of this approach are presented, including the growing role of data science and computational modeling methods in identifying drugs with potential for repositioning. Certain business aspects of drug innovation, especially the relevant factors of market exclusivity, are also discussed. Despite the promising potential of drug repositioning for innovation, it remains underutilized, especially in veterinary applications. To change this landscape for mutual benefits of human and veterinary drug innovation, further exploitation of the potency of drug repositioning is necessary through closer cooperation between all stakeholders, academia, industry, pharmaceutical authorities, and innovation policy makers, and the integration of human and veterinary repositioning into a unified innovation space. For this purpose, the establishment of the conceptually new "One Health Drug Repositioning Platform" is proposed. Oncology is one of the disease areas where this platform can significantly support the development of new drugs for human and dog (or other companion animals) anticancer therapies. As an example of the utilization of human and veterinary drugs for veterinary repositioning, the use of COX inhibitors to treat dog cancers is reviewed.

Revealing the Role of the Arg and Lys in Shifting Paradigm from BTK Selective Inhibition to the BTK/HCK Dual Inhibition - Delving into the Inhibitory Activity of KIN-8194 against BTK, and HCK in the Treatment of Mutated BTKCys481 Waldenström Macroglobulinemia: A Computational Approach

BACKGROUND

Despite the early success of Bruton's tyrosine kinase (BTK) inhibitors in the treatment of Waldenström macroglobulinemia (WM), these single-target drug therapies have limitations in their clinical applications, such as drug resistance. Several alternative strategies have been developed, including the use of dual inhibitors, to maximize the therapeutic potential of these drugs.

OBJECTIVE

Recently, the pharmacological activity of KIN-8194 was repurposed to serve as a 'dual-target' inhibitor of BTK and Hematopoietic Cell Kinase (HCK). However, the structural dual inhibitory mechanism remains unexplored, hence the aim of this study.

METHODS

Conducting predictive pharmacokinetic profiling of KIN-8194, as well as demonstrating a comparative structural mechanism of inhibition against the above-mentioned enzymes.

RESULTS

Our results revealed favourable binding affinities of -20.17 kcal/mol, and -35.82 kcal/mol for KIN-8194 towards HCK and BTK, respectively. Catalytic residues Arg137/174 and Lys42/170 in BTK and Arg303 and Lys75/173/244/247 in HCK were identified as crucial mediators of the dual binding mechanism of KIN-8194, corroborated by high per-residue energy contributions and consistent high-affinity interactions of these residues. Prediction of the pharmacokinetics and physicochemical properties of KIN-8194 further established its inhibitory potential, evidenced by the favourable absorption, metabolism, excretion, and minimal toxicity properties. Structurally, KIN-8194 impacted the stability, flexibility, solvent-accessible surface area, and rigidity of BTK and HCK, characterized by various alterations observed in the bound and unbound structures, which proved enough to disrupt their biological function.

CONCLUSION

These structural insights provided a baseline for the understanding of the dual inhibitory activity of KIN- 8194. Establishing the cruciality of the interactions between the KIN-8194 and Arg and Lys residues could guide the structure-based design of novel dual BTK/HCK inhibitors with improved therapeutic activities.

Role of Oxytocin and Vasopressin in Neuropsychiatric Disorders: Therapeutic Potential of Agonists and Antagonists

Oxytocin (OT) and vasopressin (AVP) are hypothalamic neuropeptides classically associated with their regulatory role in reproduction, water homeostasis, and social behaviors. Interestingly, this role has expanded in recent years and has positioned these neuropeptides as therapeutic targets for various neuropsychiatric diseases such as autism, addiction, schizophrenia, depression, and anxiety disorders. Due to the chemical-physical characteristics of these neuropeptides including short half-life, poor blood-brain barrier penetration, promiscuity for AVP and OT receptors (AVP-R, OT-R), novel ligands have been developed in recent decades. This review summarizes the role of OT and AVP in neuropsychiatric conditions, as well as the findings of different OT-R and AVP-R agonists and antagonists, used both at the preclinical and clinical level. Furthermore, we discuss their possible therapeutic potential for central nervous system (CNS) disorders.

Machine learning approach to discovery of small molecules with potential inhibitory action against vasoactive metalloproteases

With the advancement of combinatorial chemistry and big data, drug repositioning has boomed. In this sense, machine learning and artificial intelligence techniques offer a priori information to identify the most promising candidates. In this study, we combine QSAR and docking methodologies to identify compounds with potential inhibitory activity of vasoactive metalloproteases for the treatment of cardiovascular diseases. To develop this study, we used a database of 191 thermolysin inhibitor compounds, which is the largest as far as we know. First, we use Dragon's molecular descriptors (0-3D) to develop classification models using Bayesian networks (Naive Bayes) and artificial neural networks (Multilayer Perceptron). The obtained models are used for virtual screening of small molecules in the international DrugBank database. Second, docking experiments are carried out for all three enzymes using the Autodock Vina program, to identify possible interactions with the active site of human metalloproteases. As a result, high-performance artificial intelligence QSAR models are obtained for training and prediction sets. These allowed the identification of 18 compounds with potential inhibitory activity and an adequate oral bioavailability profile, which were evaluated using docking. Four of them showed high binding energies for the three enzymes, and we propose them as potential dual ACE/NEP inhibitors for the control of blood pressure. In summary, the in silico strategies used here constitute an important tool for the early identification of new antihypertensive drug candidates, with substantial savings in time and money.

Potential of Malawi's medicinal plants in Covid-19 disease management: A review

The Coronavirus Disease 2019 (COVID-19) caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has triggered an international pandemic that has led to significant public health problems. To date, limited evidence exists to suggest that drugs are effective against the disease. As possible treatments are being investigated, herbal medicines have shown potential for producing novel antiviral agents for the COVID-19 disease.

Aim

This review explored the potential of Malawi's traditional medicinal plants for the management of COVID-19.

Methods

The authors searched on PubMed and Google scholar for medicinal plants that are used in Malawi and published in openly available peer reviewed journals. Plants linked with antiviral treatment, anti-COVID-19 activity or COVID-19 symptoms management were targeted. These included activity against pneumonia, inflammation, cough, difficulty in breathing, pain/aches, fever, diarrhoea, rheumatism, fatigue, asthma, immunocompromised and cardiovascular diseases.

Results

11 studies were found with 306 plant species. 127 plant species had at least one COVID-19 related pharmacological activity. Of these plant species, the number of herbal entities used for each indication was: pain/aches (87), fever (2), pneumonia (9), breathing/asthma problems (5), coughing (11), diarrhoea (1), immunosuppression (8), blood issues (10), fatigue (2), heart problems (11), inflammation (8), rheumatism (10) and viral diseases (12). Thirty (30) species were used for more than one disease and Azedarachta indica topped the list (6 of the 13 COVID-19 related diseases). The majority of the species had phytochemicals known to have antiviral activity or mechanisms of actions linked to COVID-19 and consequent diseases' treatment pathways.

Conclusion

Medicinal plants are a promising source of compounds that can be used for drug development of COVID-19 related diseases. This review highlights potential targets for the World Health Organization and other research entities to explore in order to assist in controlling the pandemic.

Fine-tuning of a generative neural network for designing multi-target compounds

Exploring the origin of multi-target activity of small molecules and designing new multi-target compounds are highly topical issues in pharmaceutical research. We have investigated the ability of a generative neural network to create multi-target compounds. Data sets of experimentally confirmed multi-target, single-target, and consistently inactive compounds were extracted from public screening data considering positive and negative assay results. These data sets were used to fine-tune the REINVENT generative model via transfer learning to systematically recognize multi-target compounds, distinguish them from single-target or inactive compounds, and construct new multi-target compounds. During fine-tuning, the model showed a clear tendency to increasingly generate multi-target compounds and structural analogs. Our findings indicate that generative models can be adopted for de novo multi-target compound design.

Tackling Alzheimer's Disease with Existing Drugs: A Promising Strategy for Bypassing Obstacles

The unmet need for the development of effective drugs to treat Alzheimer 's disease has been steadily growing, representing a major challenge in drug discovery. In this context, drug repurposing, namely the identification of novel therapeutic indications for approved or investigational compounds, can be seen as an attractive attempt to obtain new medications reducing both the time and the economic burden usually required for research and development programs. In the last years, several classes of drugs have evidenced promising beneficial effects in neurodegenerative diseases, and for some of them, preliminary clinical trials have been started. This review aims to illustrate some of the most recent examples of drugs reprofiled for Alzheimer's disease, considering not only the finding of new uses for existing drugs but also the new hypotheses on disease pathogenesis that could promote previously unconsidered therapeutic regimens. Moreover, some examples of structural modifications performed on existing drugs in order to obtain multifunctional compounds will also be described.

Introducing the metacore concept for multi-target ligand design

In this work, we introduce the concept of "metacores" (MCs) for the organization of analog series (ASs) and multi-target (MT) ligand design. Generating compounds that are active against distantly related or unrelated targets is a central task in polypharmacology-oriented drug discovery. MCs are obtained by two-stage extraction of structural cores from ASs. The methodology is chemically intuitive and generally applicable. Each MC represents a set of related ASs and a template for the generation of new structures. We have systematically identified ASs that exclusively consisted of analogs with MT activity and determined their target profiles. From these ASs, a large set of 317 structurally diverse MCs was extracted, 127 of which were associated with different target families. The newly generated MCs were characterized and further prioritized on the basis of AS, compound, and target coverage. The analysis indicated that 260 MCs were pharmaceutically relevant. These MCs and the compound and target information they capture are made freely available for medicinal chemistry applications.

Structured data sets of compounds with multi-target and corresponding single-target activity from biological assays

Aim:

Providing compound data sets for promiscuity analysis with single-target (ST) and multi-target (MT) activity, taking confirmed inactivity against targets into account.

Methodology:

Compounds and target annotations are extracted from screening assays. For a given combination of targets, MT and ST compounds are identified, ensuring test data completeness.

Exemplary results & data:

A total of 1242 MT compounds active against five or more targets and 6629 corresponding ST compounds are characterized, organized and made freely available.

Limitations & next steps:

Screening campaigns typically cover a smaller target space than compounds from the medicinal chemistry literature and their activity annotations might be of lesser quality. Reported compound groups will be subjected to target set-based promiscuity analysis and predictions.

The ability of a compound to bind to multiple biological targets by defined mechanisms is termed promiscuity. Analyzing compound promiscuity helps to better understand how drugs function that are capable of interacting with multiple therapeutic targets. In drug discovery, this phenomenon is referred to as polypharmacology. Machine learning using data sets of compounds with multi-target and corresponding single-target activity aids in identifying structural features that distinguish these compounds.

Construction of an IMiD-based azide library as a kit for PROTAC research

As a promising protein degradation strategy, PROTAC technology is increasingly becoming a new star in cancer treatment. Here we report the efficient construction of an IMiD-based azide library via a quick one-step conversion of the existing IMiD-based amine library. This new azide library can act as a kit to endow PROTAC libraries with triazole moieties for various POIs through a highly effective 'click reaction' and then help to rapidly screen out lead degraders that are valuable for drug development. Its power in fleetly identifying potent degraders has been verified on two oncogenic proteins, BCR-ABL and BET, the degraders of which showed comparable potency to or even higher potency than the reported PROTACs in degrading target proteins and effectively inhibiting cancer cell proliferation.

Extending the investigation of 4-thiazolidinone derivatives as potential multi-target ligands of enzymes involved in diabetes mellitus and its long-term complications: A study with pancreatic α-amylase

Diabetes mellitus is a multifactorial disease, which is frequently complicated by the development of hyperglycaemia-induced chronic complications. The therapy of diabetes mellitus often requires combinations of two or more drugs in order both to control glycaemic levels and to prevent hyperglycaemia-induced dangerous affairs. The application of multi-target agents, which are able to control simultaneously several pathogenic mechanisms, represents a useful alternative and, in fact, their discovery is a pursued aim of the research. Some (5-arylidene-4-oxo-2-thioxothiazolidin-3-yl)acetic acids, which we had previously reported as inhibitors of selected enzymes critically implicated in diabetes mellitus, were tested against pancreatic α-amylase and intestinal α-glucosidase. These enzymes catalyse the hydrolysis of dietary oligo- and polysaccharides into monosaccharides and, consequently, are responsible for postprandial hyperglycaemia; therefore, their inhibition is one of the possible strategies to control glycaemic levels in diabetes mellitus. In addition, we investigated the aggregation tendency of the tested compounds, through direct and indirect methods, in order to evaluate the mechanism of their multiple action and discover if aggregation may contribute to the inhibition of the target enzymes. Overall, compounds 1, 3 and 4 exhibited the most favourable profile since they were shown to act as multi-target inhibitors of enzymes involved in pathways related to diabetes mellitus, without producing aggregates even at high micromolar concentrations and, therefore, can be promising agents for further developments.

Analysis of Biological Screening Compounds with Single- or Multi-Target Activity via Diagnostic Machine Learning

Predicting compounds with single- and multi-target activity and exploring origins of compound specificity and promiscuity is of high interest for chemical biology and drug discovery. We present a large-scale analysis of compound promiscuity including two major components. First, high-confidence datasets of compounds with multi- and corresponding single-target activity were extracted from biological screening data. Positive and negative assay results were taken into account and data completeness was ensured. Second, these datasets were investigated using diagnostic machine learning to systematically distinguish between compounds with multi- and single-target activity. Models built on the basis of chemical structure consistently produced meaningful predictions. These findings provided evidence for the presence of structural features differentiating promiscuous and non-promiscuous compounds. Machine learning under varying conditions using modified datasets revealed a strong influence of nearest neighbor relationship on the predictions. Many multi-target compounds were found to be more similar to other multi-target compounds than single-target compounds and vice versa, which resulted in consistently accurate predictions. The results of our study confirm the presence of structural relationships that differentiate promiscuous and non-promiscuous compounds.

Systematic Data Analysis and Diagnostic Machine Learning Reveal Differences between Compounds with Single- and Multitarget Activity

Small molecules with multitarget activity are capable of triggering polypharmacological effects and are of high interest in drug discovery. Compared to single-target compounds, promiscuity also affects drug distribution and pharmacodynamics and alters ADMET characteristics. Features distinguishing between compounds with single- and multitarget activity are currently only little understood. On the basis of systematic data analysis, we have assembled large sets of promiscuous compounds with activity against related or functionally distinct targets and the corresponding compounds with single-target activity. Machine learning predicted promiscuous compounds with surprisingly high accuracy. Molecular similarity analysis combined with control calculations under varying conditions revealed that accurate predictions were largely determined by structural nearest-neighbor relationships between compounds from different classes. We also found that large proportions of promiscuous compounds with activity against related or unrelated targets and corresponding single-target compounds formed analog series with distinct chemical space coverage, which further rationalized the predictions. Moreover, compounds with activity against proteins from functionally distinct classes were often active against unique targets that were not covered by other promiscuous compounds. The results of our analysis revealed that nearest-neighbor effects determined the prediction of promiscuous compounds and that preferential partitioning of compounds with single- and multitarget activity into structurally distinct analog series was responsible for such effects, hence providing a rationale for the presence of different structure-promiscuity relationships.

Drugs, host proteins and viral proteins: how their promiscuities shape antiviral design

The reciprocal nature of drug specificity and target specificity implies that the same is true for their respective promiscuities. Protein promiscuity has two broadly different types of footprint in drug design. The first is relaxed specificity of binding sites for substrates, inhibitors, effectors or cofactors. The second involves protein-protein interactions of regulatory processes such as signal transduction and transcription, and here protein intrinsic disorder plays an important role. Both viruses and host cells exploit intrinsic disorder for their survival, as do the design and discovery programs for antivirals. Drug action, strictly speaking, always relies upon promiscuous activity, with drug promiscuity enlarging its scope. Drug repurposing searches for additional promiscuity on the part of both the drug and the target in the host. Understanding the subtle nuances of these promiscuities is critical in the design of novel and more effective antivirals.

Repositioning Dequalinium as Potent Muscarinic Allosteric Ligand by Combining Virtual Screening Campaigns and Experimental Binding Assays

Structure-based virtual screening is a truly productive repurposing approach provided that reliable target structures are available. Recent progresses in the structural resolution of the G-Protein Coupled Receptors (GPCRs) render these targets amenable for structure-based repurposing studies. Hence, the present study describes structure-based virtual screening campaigns with a view to repurposing known drugs as potential allosteric (and/or orthosteric) ligands for the hM₂ muscarinic subtype which was indeed resolved in complex with an allosteric modulator thus allowing a precise identification of this binding cavity. First, a docking protocol was developed and optimized based on binding space concept and enrichment factor optimization algorithm (EFO) consensus approach by using a purposely collected database including known allosteric modulators. The so-developed consensus models were then utilized to virtually screen the DrugBank database. Based on the computational results, six promising molecules were selected and experimentally tested and four of them revealed interesting affinity data; in particular, dequalinium showed a very impressive allosteric modulation for hM₂. Based on these results, a second campaign was focused on bis-cationic derivatives and allowed the identification of other two relevant hM₂ ligands. Overall, the study enhances the understanding of the factors governing the hM₂ allosteric modulation emphasizing the key role of ligand flexibility as well as of arrangement and delocalization of the positively charged moieties.

Epigenetic polypharmacology: A new frontier for epi-drug discovery

Recently, despite the great success achieved by the so-called "magic bullets" in the treatment of different diseases through a marked and specific interaction with the target of interest, the pharmacological research is moving toward the development of "molecular network active compounds," embracing the related polypharmacology approach. This strategy was born to overcome the main limitations of the single target therapy leading to a superior therapeutic effect, a decrease of adverse reactions, and a reduction of potential mechanism(s) of drug resistance caused by robustness and redundancy of biological pathways. It has become clear that multifactorial diseases such as cancer, neurological, and inflammatory disorders, may require more complex therapeutic approaches hitting a certain biological system as a whole. Concerning epigenetics, the goal of the multi-epi-target approach consists in the development of small molecules able to simultaneously and (often) reversibly bind different specific epi-targets. To date, two dual histone deacetylase/kinase inhibitors (CUDC-101 and CUDC-907) are in an advanced stage of clinical trials. In the last years, the growing interest in polypharmacology encouraged the publication of high-quality reviews on combination therapy and hybrid molecules. Hence, to update the state-of-the-art of these therapeutic approaches avoiding redundancy, herein we focused only on multiple medication therapies and multitargeting compounds exploiting epigenetic plus nonepigenetic drugs reported in the literature in 2018. In addition, all the multi-epi-target inhibitors known in literature so far, hitting two or more epigenetic targets, have been included.

Triazol: a privileged scaffold for proteolysis targeting chimeras

Current traditional drugs such as enzyme inhibitors and receptor agonists/antagonists present inherent limitations due to occupancy-driven pharmacology as the mode of action. Proteolysis targeting chimeras (PROTACs) are composed of an E3 ligand, a connecting linker and a target protein ligand, and are an attractive approach to specifically knockdown-targeted proteins utilizing an event-driven mode of action. The length, hydrophilicity and rigidity of connecting linkers play important role in creating a successful PROTAC. Some PROTACs with a triazole linker have displayed promising anticancer activity. This review provides an overview of PROTACs with a triazole scaffold and discusses its structure-activity relationship. Important milestones in the development of PROTACs are addressed and a critical analysis of this drug discovery strategy is also presented.

High Impact: The Role of Promiscuous Binding Sites in Polypharmacology

The literature focuses on drug promiscuity, which is a drug’s ability to bind to several targets, because it plays an essential role in polypharmacology. However, little work has been completed regarding binding site promiscuity, even though its properties are now recognized among the key factors that impact drug promiscuity. Here, we quantified and characterized the promiscuity of druggable binding sites from protein-ligand complexes in the high quality Mother Of All Databases while using statistical methods. Most of the sites (80%) exhibited promiscuity, irrespective of the protein class. Nearly half were highly promiscuous and able to interact with various types of ligands. The corresponding pockets were rather large and hydrophobic, with high sulfur atom and aliphatic residue frequencies, but few side chain atoms. Consequently, their interacting ligands can be large, rigid, and weakly hydrophilic. The selective sites that interacted with one ligand type presented less favorable pocket properties for establishing ligand contacts. Thus, their ligands were highly adaptable, small, and hydrophilic. In the dataset, the promiscuity of the site rather than the drug mainly explains the multiple interactions between the drug and target, as most ligand types are dedicated to one site. This underlines the essential contribution of binding site promiscuity to drug promiscuity between different protein classes.

3D-PP: A Tool for Discovering Conserved Three-Dimensional Protein Patterns

Discovering conserved three-dimensional (3D) patterns among protein structures may provide valuable insights into protein classification, functional annotations or the rational design of multi-target drugs. Thus, several computational tools have been developed to discover and compare protein 3D-patterns. However, most of them only consider previously known 3D-patterns such as orthosteric binding sites or structural motifs. This fact makes necessary the development of new methods for the identification of all possible 3D-patterns that exist in protein structures (allosteric sites, enzyme-cofactor interaction motifs, among others). In this work, we present 3D-PP, a new free access web server for the discovery and recognition all similar 3D amino acid patterns among a set of proteins structures (independent of their sequence similarity). This new tool does not require any previous structural knowledge about ligands, and all data are organized in a high-performance graph database. The input can be a text file with the PDB access codes or a zip file of PDB coordinates regardless of the origin of the structural data: X-ray crystallographic experiments or in silico homology modeling. The results are presented as lists of sequence patterns that can be further analyzed within the web page. We tested the accuracy and suitability of 3D-PP using two sets of proteins coming from the Protein Data Bank: (a) Zinc finger containing and (b) Serotonin target proteins. We also evaluated its usefulness for the discovering of new 3D-patterns, using a set of protein structures coming from in silico homology modeling methodologies, all of which are overexpressed in different types of cancer. Results indicate that 3D-PP is a reliable, flexible and friendly-user tool to identify conserved structural motifs, which could be relevant to improve the knowledge about protein function or classification. The web server can be freely utilized at https://appsbio.utalca.cl/3d-pp/.

Exploration of interaction scoring criteria in the CANDO platform

OBJECTIVE

Ascertain the optimal interaction scoring criteria for the Computational Analysis of Novel Drug Opportunities (CANDO) platform for shotgun drug repurposing to improve benchmarking performance, thereby enabling more accurate prediction of novel therapeutic drug-indication pairs.

RESULTS

We have investigated and enhanced the interaction scoring criteria in the bioinformatic docking protocol in the newest version of our platform (v1.5), with the best performing interaction scoring criterion yielding increased benchmarking accuracies from 11.7% in v1 to 12.8% in v1.5 at the top10 cutoff (the most stringent one) and correspondingly from 24.9 to 31.2% at the top100 cutoff.

Appendix A. dithioloquinolinethiones as new potential multitargeted antibacterial and antifungal agents: Synthesis, biological evaluation and molecular docking studies

Herein we report the design, synthesis, molecular docking study and evaluation of antimicrobial activity of ten new dithioloquinolinethiones. The structures of compounds were confirmed by ¹H NMR, ¹³C NMR and HPLC-HRMS. Before evaluation of their possible antimicrobial activity prediction of toxicity was performed. All compounds showed antibacterial activity against eight Gram positive and Gram negative bacterial species. All compounds appeared to be more active than ampicillin and almost all than streptomycin. The best antibacterial activity was observed for compound 8c 4,4,8-trimethyl-5-{[(4-phenyl-5-thioxo-4,5-dihydro-1,3,4-thiadiazol-2-yl)thio]acetyl}-4,5-dihydro-1H-[1,2]dithiolo[3,4c]quino lone-1-thione). The most sensitive bacterium En.cloacae followed by S. aureus, while L.monocytogenes was the most resistant. All compounds were tested for antifungal activity also against eight fungal species. The best activity was expressed by compound 8d (5-[(4,5-Dihydro-1,3-thiazol-2-ylthio)acetyl]-4,4-dimethyl-4,5-dihydro-1H-[1,2]dithiolo[3,4-c]quinoline-1-thione). The most sensitive fungal was T. viride, while P. verrucosum var. cyclopium was the most resistant one. All compounds were more potent as antifungal agent than reference compound bifonazole and ketoconazole. The docking studies indicated a probable involvement of E. coli DNA GyrB inhibition in the anti-bacterial mechanism, while CYP51ca inhibition is probably responsible for antifungal activity of tested compounds. It is interesting to mention that docking results coincides with experimental.