How drug-like are ‘ugly’ drugs: do drug-likeness metrics predict ADME behaviour in humans?

Prediction of Drug-Induced Liver Injury: From Molecular Physicochemical Properties and Scaffold Architectures to Machine Learning Approaches

The process of developing new drugs is widely acknowledged as being time-intensive and requiring substantial financial investment. Despite ongoing efforts to reduce time and expenses in drug development, ensuring medication safety remains an urgent problem. One of the major problems involved in drug development is hepatotoxicity, specifically known as drug-induced liver injury (DILI). The popularity of new drugs often poses a significant barrier during development and frequently leads to their recall after launch. In silico methods have many advantages compared with traditional in vivo and in vitro assays. To establish a more precise and reliable prediction model, it is necessary to utilize an extensive and high-quality database consisting of information on drug molecule properties and structural patterns. In addition, we should also carefully select appropriate molecular descriptors that can be used to accurately depict compound characteristics. The aim of this study was to conduct a comprehensive investigation into the prediction of DILI. First, we conducted a comparative analysis of the physicochemical properties of extensively well-prepared DILI-positive and DILI-negative compounds. Then, we used classic substructure dissection methods to identify structural pattern differences between these two different types of chemical molecules. These findings indicate that it is not feasible to establish property or substructure-based rules for distinguishing between DILI-positive and DILI-negative compounds. Finally, we developed quantitative classification models for predicting DILI using the naïve Bayes classifier (NBC) and recursive partitioning (RP) machine learning techniques. The optimal DILI prediction model was obtained using NBC, which combines 21 physicochemical properties, the VolSurf descriptors and the LCFP_10 fingerprint set. This model achieved a global accuracy (GA) of 0.855 and an area under the curve (AUC) of 0.704 for the training set, while the corresponding values were 0.619 and 0.674 for the test set, respectively. Moreover, indicative substructural fragments favorable or unfavorable for DILI were identified from the best naïve Bayesian classification model. These findings may help prioritize lead compounds in the early stage of drug development pipelines.

In vivo anti-gastric ulcer activity of 7-O-methyl aromadendrin and sakuranetin via mitigating inflammatory and oxidative stress trails

ETHNOPHARMACOLOGICAL RELEVANCE

Eucalyptus genus has been used for a very long time in conventional treatment as an anti-ulcer remedy.

AIM OF THE STUDY

The study aimed to explore the gastroprotective potential of 7-O-methyl aromadendrin (7-OMA), and sakuranetin (SKN) in comparison with omeprazole. The study tackled the contribution of their anti-inflammatory, antioxidant, and antiapoptotic capabilities to their anti-gastric ulcer effects.

MATERIALS AND METHODS

An ethanol-induced gastric ulcer model in rats was adopted and the consequences were confirmed by a molecular docking study.

RESULTS

The oral pretreatment of rats 1 h before ethanol using omeprazole (20 mg/kg) or 7-OMA (20 or 40 mg/kg) or SKN (20 or 40 mg/kg) exhibited gastroprotective and anti-inflammatory properties to different extents. These amendments witnessed as restorations in the stomach histological architecture in H and E-stained sections, mucus content in periodic acid-Schiff (PAS) stained sections with increased cellular proliferation, as demonstrated by increased immunohistochemical staining of PCNA, and increments in stomach COX-1 activity and eNOS. The highest dose of SKN showed the best corrections to reach 4.8, 1.8, and 2.1 folds increase in PAS, COX-1 and eNOS, respectively as compared to the untreated ethanol-induced gastric ulcer group; effects that were comparable to that of omeprazole. Moreover, reductions in COX-2 activity, and the protein expression of NF-κB, IL-6, TNF-α and NOx, in addition to the gene expression of inducible iNOS were also noted. Moreover, the antioxidant and antiapoptotic capabilities of omeprazole, 7-OMA, and SKN were perceived. SKN (40 mg/kg) succeeded to show the unsurpassed results to reach 293.6%, 237.1%, 274.7%, 248.2%, and 175.4% in total and reduced GSH, catalase, SOD, and Bcl2, respectively, as well as 50.0%, 46.8%, and 52.1 % in oxidized GSSG, TBARS and caspase-3, respectively. The gastroprotective potential of the tested compounds can be assigned to their anti-inflammatory, antioxidant and antiapoptotic properties.7-OMA and SKN were studied using molecular docking into the binding sites of the most significant inflammatory targets, including COX-2, TNF-α, iNOS, and NF-κB. Pharmacokinetic and physicochemical parameters in silico were appropriate.

CONCLUSION

The prophylactic use of 7-OMA and SKN could be considered as an add-on to recurrent gastric ulcers and might influence its therapeutic approaches.

The Discovery of New Inhibitors of Insulin-Regulated Aminopeptidase by a High-Throughput Screening of 400,000 Drug-like Compounds

With the ambition to identify novel chemical starting points that can be further optimized into small drug-like inhibitors of insulin-regulated aminopeptidase (IRAP) and serve as potential future cognitive enhancers in the clinic, we conducted an ultra-high-throughput screening campaign of a chemically diverse compound library of approximately 400,000 drug-like small molecules. Three biochemical and one biophysical assays were developed to enable large-scale screening and hit triaging. The screening funnel, designed to be compatible with high-density microplates, was established with two enzyme inhibition assays employing either fluorescent or absorbance readouts. As IRAP is a zinc-dependent enzyme, the remaining active compounds were further evaluated in the primary assay, albeit with the addition of zinc ions. Rescreening with zinc confirmed the inhibitory activity for most compounds, emphasizing a zinc-independent mechanism of action. Additionally, target engagement was confirmed using a complementary biophysical thermal shift assay where compounds causing positive/negative thermal shifts were considered genuine binders. Triaging based on biochemical activity, target engagement, and drug-likeness resulted in the selection of 50 qualified hits, of which the IC50 of 32 compounds was below 3.5 µM. Despite hydroxamic acid dominance, diverse chemotypes with biochemical activity and target engagement were discovered, including non-hydroxamic acid compounds. The most potent compound (QHL1) was resynthesized with a confirmed inhibitory IC50 of 320 nM. Amongst these compounds, 20 new compound structure classes were identified, providing many new starting points for the development of unique IRAP inhibitors. Detailed characterization and optimization of lead compounds, considering both hydroxamic acids and other diverse structures, are in progress for further exploration.

In vitro-in silico pharmacology and chemistry of Stercularin, isolated from Sterculia diversifolia

Stercularin is a coumarin, isolated from the ethyl acetate fraction of stem bark and leaves of S. diversifolia. Pharmacologically it is active against cancer, diabetes, and inflammation etc. The molecule is further screened for in vitro pharmacological activities. In addition, a detailed description on its drug likeness and pharmacokinetic profile has been established to further explore its fate as a drug candidate. Stercularin exhibited antiglycation, immunomodulatory, and leishmanicidal activity in three different in vitro models. The IC50 values obtained in these three assays were 80.22 ± 0.46 mg/ml, 12.8 ± 1.6 μg/ml, and 8.32 ± 0.42 μg/ml, respectively. In case of drug likeness evaluation, Stercularin has acceptable physicochemical properties and compliant with major drug likeness descriptors i.e., Lipinski rule, Pfizer rule, GSK rule, and "golden triangle". Accepting Lipinski rule implies the oral drug development of Stercularin. Pharmacokinetically, Stercularin is permeable to Caco-2 and MDCK cell lines. 'Boiled-egg' plot suggest intestinal route of absorption, blood brain barrier nonpermeating, and not affected by p-glycoprotein. Stercularin has high plasma protein binding with low free fraction circulating in the plasma. Stercularin proved to be the substrate and/or inhibitor of CYP 450 system with a moderate half-life and clearance rate to allow flexible dosing regimen. Finally, slight risk of toxicity exists for Stercularin, but not being limiting factors of drug knock out. A nature isolated Stercularin possess pharmacological activities and is predicted to have acceptable pharmacokinetic profile. Further drug development and in vivo studies are desirable for optimization.

Design, synthesis, and biological evaluation of novel pyrimidin-2-amine derivatives as potent PLK4 inhibitors

Serine/threonine protein kinase PLK4 is a master regulator of centriole duplication, which is significant for maintaining genome integrity. Accordingly, due to the detection of PLK4 overexpression in a variety of cancers, PLK4 has been identified as a candidate anticancer target. Thus, it is a very meaningful to find effective and safe PLK4 inhibitors for the treatment of cancer. However, the reported PLK4 inhibitors are scarce and have potential safety issues. In this study, a series of novel and potent PLK4 inhibitors with an aminopyrimidine core was obtained utilizing the scaffold hopping strategy. The in vitro enzyme activity results showed that compound 8h (PLK4 IC50 = 0.0067 μM) displayed high PLK4 inhibitory activity. In addition, compound 8h exhibited a good plasma stability (t1/2 > 289.1 min), liver microsomal stability (t1/2 > 145 min), and low risk of DDIs. At the cellular level, it presented excellent antiproliferative activity against breast cancer cells. Taken together, these results suggest that compound 8h has potential value in the further research of PLK4-targeted anticancer drugs.

Impact of Applicability Domains to Generative Artificial Intelligence

Molecular generative artificial intelligence is drawing significant attention in the drug design community, with several experimentally validated proof of concepts already published. Nevertheless, generative models are known for sometimes generating unrealistic, unstable, unsynthesizable, or uninteresting structures. This calls for methods to constrain those algorithms to generate structures in drug-like portions of the chemical space. While the concept of applicability domains for predictive models is well studied, its counterpart for generative models is not yet well-defined. In this work, we empirically examine various possibilities and propose applicability domains suited for generative models. Using both public and internal data sets, we use generative methods to generate novel structures that are predicted to be actives by a corresponding quantitative structure-activity relationships model while constraining the generative model to stay within a given applicability domain. Our work looks at several applicability domain definitions, combining various criteria, such as structural similarity to the training set, similarity of physicochemical properties, unwanted substructures, and quantitative estimate of drug-likeness. We assess the structures generated from both qualitative and quantitative points of view and find that the applicability domain definitions have a strong influence on the drug-likeness of generated molecules. An extensive analysis of our results allows us to identify applicability domain definitions that are best suited for generating drug-like molecules with generative models. We anticipate that this work will help foster the adoption of generative models in an industrial context.

Physicochemical Properties, Drug Likeness, ADMET, DFT Studies and in vitro antioxidant activity of Oxindole Derivatives

Poor pharmacokinetic and safety profiles create significant hurdles in the drug development process. This work focuses on a detailed understanding of drug discovery interplay among physicochemical, pharmacokinetic, toxicity endpoints, and antioxidant properties of oxindole derivatives. DFT compıutations were also performed at B3LYP/6-311G** level to evaluate the physicochemical properties, global reactivity features, and intramolecular interactions. The BOILED-Egg pharmacokinetic model envisaged gastrointestinal absorption, blood-brain barrier penetration, and no interaction with p-glycoprotein for compounds C1 and C2. The physicochemical evaluation revealed that C1 possesses superior drug-like properties fit for oral absorption. Both derivatives were predicted to have high plasma protein binding, efficient distribution, and inhibiting CYP 450 major isoforms but serve as substrates only for a few of them. Both molecules have mild to moderate clearance rates. Out of ten toxicity parameters, only hepatotoxicity was predicted. DFT results implied that the meta position of the -OH group made the possibility of charge transfer greater than -para positioned -OH, due to the ΔN_max (eV) values of molecules C1 and C2 being calculated at 2.596 and 2.477, respectively. Both C1 and C2 exhibited a concentration dependant DPPH and ABTS radical scavenging activity. The chemical structure-physicochemical-pharmacokinetic relationship identified the meta position as the favorite for the electron-withdrawing hydroxyl group. This provides useful insight to medicinal chemists to design 6-chlorooxindole derivatives with an acceptable drug-like and pharmacokinetic property.

Systematic investigation of the mechanism of herbal medicines for the treatment of prostate cancer

Due to various unpleasant side effects and general ineffectiveness of current treatments for prostate cancer (PCa), more and more people with PCa try to look for complementary and alternative medicine such as herbal medicine. However, since herbal medicine has multi-components, multi-targets and multi-pathways features, its underlying molecular mechanism of action is not yet known and still needs to be systematically explored. Presently, a comprehensive approach consisting of bibliometric analysis, pharmacokinetic assessment, target prediction and network construction is firstly performed to obtain PCa-related herbal medicines and their corresponding candidate compounds and potential targets. Subsequently, a total of 20 overlapping genes between DEGs in PCa patients and the target genes of the PCa-related herbs, as well as five hub genes, i.e., CCNA2, CDK2, CTH, DPP4 and SRC were determined employing bioinformatics analysis. Further, the roles of these hub genes in PCa were also investigated through survival analysis and tumour immunity analysis. Moreover, to validate the reliability of the C-T interactions and to further explore the binding modes between ingredients and their targets, the molecular dynamics (MD) simulations were carried out. Finally, based on the modularization of the biological network, four signaling pathways, i.e., PI3K-Akt, MAPK, p53 and cell cycle were integrated to further analyze the therapeutic mechanism of PCa-related herbal medicine. All the results show the mechanism of action of herbal medicines on treating PCa from the molecular to systematic levels, providing a reference for the treatment of complex diseases using TCM.

In silico and in vitro anti-inflammatory study of phenolic compounds isolated from Eucalyptus maculata resin

Plant resins are rich in bioactive compounds with high medicinal values. However, the chemistry and anti-inflammatory activity of the resins produced by trees of the genus Eucalyptus were scarcely investigated. The inflammatory targets cyclooxygenase-1 (COX-1), COX-2, TNF-, NF-B, and NO were significantly inhibited by the methanolic extract of Eucalyptus maculata kino resin (EME) and its CH₂Cl₂ soluble fraction (MCF). Sakuranetin (C1), (E)-cinnamic acid (C2), kaempferol 7- methyl ether (C3), 7-O-methyl aromadendrin (C4), and 1,6- dicinnamoyl-O-α-D-glucopyranoside (C5) were isolated from MCF. Three compounds (C1, C2, and C4) showed potent in vitro COX-1 inhibition, while C5 inhibited COX-2, TNF-α, NF-κB, and NO significantly. An in-silico study revealed that C5 had the highest binding affinity to the active site in COX-2 with binding energy score (S) of -14.85 kcal/mol, better than celecoxib (COX-2 inhibitor). In conclusion, 1,6-dicinnamoyl-O-α-D-glucopyranoside (C5) could be investigated further in the search for anti-inflammatory agents.

Deciphering Nonbioavailable Substructures Improves the Bioavailability of Antidepressants by Serotonin Transporter

Inadequate bioavailability is one of the most critical reasons for the failure of oral drug development. However, the way that substructures affect bioavailability remains largely unknown. Serotonin transporter (SERT) inhibitors are first-line drugs for major depression disorder, and improving their bioavailability may be able to decrease side-effects by reducing daily dose. Thus, it is an excellent model to probe the relationship between substructures and bioavailability. Here, we proposed the concept of "nonbioavailable substructures", referring to substructures that are unfavorable to bioavailability. A machine learning model was developed to identify nonbioavailable substructures based on their molecular properties and shows the accuracy of 83.5%. A more potent SERT inhibitor DH4 was discovered with a bioavailability of 83.28% in rats by replacing the nonbioavailable substructure of approved drug vilazodone. DH4 exhibits promising anti-depression efficacy in animal experiments. The concept of nonbioavailable substructures may open up a new venue for the improvement of drug bioavailability.

Computational pharmacology and computational chemistry of 4-hydroxyisoleucine: Physicochemical, pharmacokinetic, and DFT-based approaches

Computational pharmacology and chemistry of drug-like properties along with pharmacokinetic studies have made it more amenable to decide or predict a potential drug candidate. 4-Hydroxyisoleucine is a pharmacologically active natural product with prominent antidiabetic properties. In this study, ADMETLab 2.0 was used to determine its important drug-related properties. 4-Hydroxyisoleucine is compliant with important drug-like physicochemical properties and pharma giants' drug-ability rules like Lipinski's, Pfizer, and GlaxoSmithKline (GSK) rules. Pharmacokinetically, it has been predicted to have satisfactory cell permeability. Blood-brain barrier permeation may add central nervous system (CNS) effects, while a very slight probability of being CYP2C9 substrate exists. None of the well-known toxicities were predicted in silico, being congruent with wet lab results, except for a "very slight risk" for respiratory toxicity predicted. The molecule is non ecotoxic as analyzed with common indicators such as bioconcentration and LC₅₀ for fathead minnow and daphnia magna. The toxicity parameters identified 4-hydroxyisoleucine as non-toxic to androgen receptors, PPAR-γ, mitochondrial membrane receptor, heat shock element, and p53. However, out of seven parameters, not even a single toxicophore was found. The density functional theory (DFT) study provided support to the findings obtained from drug-like property predictions. Hence, it is a very logical approach to proceed further with a detailed pharmacokinetics and drug development process for 4-hydroxyisoleucine.

Uncovering the efficacy and mechanisms of Genkwa flos and bioactive ingredient genkwanin against L. monocytogenes infection

ETHNOPHARMACOLOGICAL RELEVANCE

Genkwa flos (yuanhua in Chinese), the dried flower buds of the plant Daphne genkwa Siebold & Zucc., as a traditional herb widely used for the treatment of inflammation-related symptoms and diseases, with the efficacies of diuretic, phlegm-resolving and cough suppressant.

AIM OF THE STUDY

Traditional Chinese Medicine (TCM) is presumed to be of immense potential against pathogens infection. Whereas, the potential efficacy and mechanisms of Genkwa flos against L. monocytogenes infection has not been extensively explored. The present study aimed to identify the bioactive ingredients of Genkwa flos against L. monocytogenes infection and to delineate the underlying mechanisms of action.

MATERIALS AND METHODS

Bioinformatics approach at protein network level was employed to investigate the therapeutic mechanisms of Genkwa flos against L. monocytogenes infection. And hemolysis inhibition assay, cytoprotection test, western blotting, oligomerization assay and molecular docking analysis were applied to substantiate the multiple efficacies of Genkwa flos and the bioactive ingredient genkwanin. Histopathological analysis and biochemistry detection were conducted to evaluate the in vivo protective effect of genkwanin.

RESULTS

Network pharmacology and experimental validation revealed that Traditional Chinese Medicine (TCM) Genkwa flos exhibited anti-L. monocytogenes potency and was found to inhibit the hemolytic activity of LLO. Bioactive ingredient genkwanin interfered with the pore-forming activity of LLO by engaging the active residues Tyr414, Tyr98, Asn473, Val100, Tyr440 and Val438, and thereby attenuated LLO-mediated cytotoxicity. Consistent with the bioinformatics prediction, exposed to genkwanin could upregulate the Nrf2 level and promote the translocation of Nrf2. In vivo, genkwanin oral administration (80 mg/kg) significantly protected against systemic L. monocytogenes infection, as evidenced by reduced myeloperoxidase (MPO) and malondialdehyde (MDA) levels, increased mice survival rate by 30% and decreased pathogen colonization.

CONCLUSION

Our study demonstrated that Genkwa flos is a potential anti-L. monocytogenes TCM, highlighted the therapeutic potential of Genkwa flos active ingredient genkwanin by targeting the pore-forming cytolysin LLO and acting as a promising antioxidative candidate against L. monocytogenes infection.

Drug-like Properties and Fraction Lipophilicity Index as a combined metric

Fraction Lipophicity Index (FLI) has been developed as a composite drug-like metric combining log P and log D in a weighted manner. In the present study, an extended data set confirmed the previously established drug-like FLI range 0-8 using two calculation systems for log P/log D assessment, the freeware MedChem Designer and ClogP. The dataset was split into two classes according to the percentage of fraction absorbed (%FA) - class 1 including drugs with high to medium absorption levels and class 2 including poorly absorbed drugs. The FLI and FLI-C (ClogP based FLI) drug-like range covers 92 % and 91 % of class 1 drugs, respectively. Using MlogP, a narrower drug-like FLI-M range 0-7 was established, covering 91 % of class 1 drugs. The dependence of the degree of ionization to intrinsic lipophilicity within the FLI (FLI-C, FLI-M) drug-like range as well as the inter-relation between the other Ro5 properties (Mw, HD, HA) was explored to define drug-like / non-drug-like combinations as a safer alternative to single properties for drug candidates' prioritization. In this sense, we propose a combined metric of Mw and the number of polar atoms (Mw/NO) to account for both size and polarity. Setting the value 50 as cutoff, a distinct differentiation between class 1 and class 2 drugs was obtained with Mw/NO>50 for more than 70 % of class 1 drugs, while the opposite was observed for class 2 drugs.

Identification and analysis of a selective DYRK1A inhibitor

The dysregulation of DYRK1A is implicated in many diseases such as cancer, diabetes, and neurodegenerative diseases. Alzheimer's disease is one of the most common neurodegenerative disease and has elevated interest in DYRK1A research. Overexpression of DYRK1A has been linked to the formation of tau aggregates. Currently, an effective therapeutic treatment that targets DYRK1A is lacking. A specific small-molecule inhibitor would further our understanding of the physiological role of DYRK1A in neurodegenerative diseases and could be presented as a possible therapeutic option. In this study, we identified pharmacological interactions within the DYRK1A active site and performed a structure-based virtual screening approach to identify a selective small-molecule inhibitor. Several compounds were selected in silico for enzymatic and cellular assays, yielding a novel inhibitor. A structure-activity relationship analysis was performed to identify areas of interactions for the compounds selected in this study. When tested in vitro, reduction of DYRK1A dependent phosphorylation of tau was observed for active compounds. The active compounds also improved tau turbidity, suggesting that these compounds could alleviate aberrant tau aggregation. Testing the active compound against a panel of kinases across the kinome revealed greater selectivity towards DYRK1A. Our study demonstrates a serviceable protocol that identified a novel and selective DYRK1A inhibitor with potential for further study in tau-related pathologies.

Structure- and Ligand-Based in silico Studies towards the Repurposing of Marine Bioactive Compounds to Target SARS-CoV-2

This work was a structured virtual screening for marine bioactive compounds with reported antiviral activities which were subjected to structure-based studies against SARS-CoV-2 co-crystallized proteins. The molecular docking of marine bioactive compounds against the main protease (Mpro, PDB ID: 6lu7 and 6y2f), the spike glycoprotein (PDB ID: 6vsb), and the RNA polymerase (PDB ID: 6m71) of SARS-CoV-2 was performed. Ligand-based approach with the inclusion of rapid overlay chemical structures (ROCS) was also addressed in order to examine the probability of these marine compounds sharing relevance and druggability with the reported drugs. Among the examined marine library, the highest scores in different virtual screening aspects were displayed by compounds with flavonoids core, acyl indole, and pyrrole carboxamide alkaloids. Moreover, a complete overlay with the co-crystallized ligands of Mpro was revealed by sceptrin and debromo-sceptrin. Thalassoilin (A-B) which was found in the Red Sea exhibited the highest binding and similarity outcomes among all target proteins. These data highlight the importance of marine natural metabolites in regard to further studies for discovering new drugs to combat the COVID-19 pandemic.

The influence of calculated physicochemical properties of compounds on their ADMET profiles

We analyzed the influence of calculated physicochemical properties of more than 20,000 compounds on their P-gp and BCRP mediated efflux, microsomal stability, hERG inhibition, and plasma protein binding. Our goal was to provide guidance for designing compounds with desired pharmacokinetic profiles. Our analysis showed that compounds with ClogP less than 3 and molecular weight less than 400 will have high microsomal stability and low plasma protein binding. Compounds with logD less than 2.2 and/or basic pKa larger than 5.3 are likely to be BCRP substrates and compounds with basic pKa less than 5.2 and/or acidic pKa less than 13.4 are less likely to inhibit hERG. Based on these results, compounds with MW < 400, ClogP < 3, basic pKa < 5.2 and acidic pKa < 13.4 are likely to have good bioavailability and low hERG inhibition.

Design, synthesis and molecular modeling of novel aryl carboximidamides and 3-aryl-1,2,4-oxadiazoles derived from indomethacin as potent anti-inflammatory iNOS/PGE2 inhibitors

The development of NSAIDs/iNOS inhibitor hybrids is a new strategy for the treatment of inflammatory diseases by suppression of the overproduction of PGE₂ and NO. A novel series of aryl carboximidamides 4a-g and their cyclized 3-aryl-1,2,4-oxadiazoles 5a-g counterparts derived from indomethacin 1 were synthesized. Most of the target compounds displayed lower LPS-induced NO production IC₅₀ in RAW 264.7 cells and potent in vitro iNOS and PGE2 inhibitory activity than indomethacin. Moreover, in carrageenan-induced rat paw oedema method, most of them exhibited higher in vivo anti-inflammatory activity than the reference drug indomethacin. Notably, 4 hrs after carrageenan injection, compound 4a proved to be the most potent anti-inflammatory agent in this study, with almost two- and eight-fold more active than the reference drugs indomethacin (1) and celecoxib, respectively. Compound 4a proved to be inhibitor to LPS-induced NO production, iNOS activity and PGE2 with IC₅₀ of 10.70 μM, 2.31 μM, and 29 nM; respectively. Compounds 4a and 5b possessed the lowest ulcerogenic liabilities (35% and 38%, respectively) compared to 1. Histopathological analysis revealed that compounds 4a and 5b demonstrated reduced degeneration and healing of ulcers. Molecular docking studies into the catalytic binding pocket of the iNOS protein receptor (PDB ID: 1r35) showed good correlation with the obtained biological results. Parameters of Lipinski's rule of five and ADMET analysis were calculated where compound 4a had reasonable drug-likeness with acceptable physicochemical properties so it could be used as promising orally absorbed anti-inflammatory therapy and entitled to be used as future template for further investigations.

RNA Drugs and RNA Targets for Small Molecules: Principles, Progress, and Challenges

RNA-based therapies, including RNA molecules as drugs and RNA-targeted small molecules, offer unique opportunities to expand the range of therapeutic targets. Various forms of RNAs may be used to selectively act on proteins, transcripts, and genes that cannot be targeted by conventional small molecules or proteins. Although development of RNA drugs faces unparalleled challenges, many strategies have been developed to improve RNA metabolic stability and intracellular delivery. A number of RNA drugs have been approved for medical use, including aptamers (e.g., pegaptanib) that mechanistically act on protein target and small interfering RNAs (e.g., patisiran and givosiran) and antisense oligonucleotides (e.g., inotersen and golodirsen) that directly interfere with RNA targets. Furthermore, guide RNAs are essential components of novel gene editing modalities, and mRNA therapeutics are under development for protein replacement therapy or vaccination, including those against unprecedented severe acute respiratory syndrome coronavirus pandemic. Moreover, functional RNAs or RNA motifs are highly structured to form binding pockets or clefts that are accessible by small molecules. Many natural, semisynthetic, or synthetic antibiotics (e.g., aminoglycosides, tetracyclines, macrolides, oxazolidinones, and phenicols) can directly bind to ribosomal RNAs to achieve the inhibition of bacterial infections. Therefore, there is growing interest in developing RNA-targeted small-molecule drugs amenable to oral administration, and some (e.g., risdiplam and branaplam) have entered clinical trials. Here, we review the pharmacology of novel RNA drugs and RNA-targeted small-molecule medications, with a focus on recent progresses and strategies. Challenges in the development of novel druggable RNA entities and identification of viable RNA targets and selective small-molecule binders are discussed. SIGNIFICANCE STATEMENT: With the understanding of RNA functions and critical roles in diseases, as well as the development of RNA-related technologies, there is growing interest in developing novel RNA-based therapeutics. This comprehensive review presents pharmacology of both RNA drugs and RNA-targeted small-molecule medications, focusing on novel mechanisms of action, the most recent progress, and existing challenges.

An in silico perception for newly isolated flavonoids from peach fruit as privileged avenue for a countermeasure outbreak of COVID-19

3'-Hydroxy-4'-methoxy-chroman-7-O-β-d-glucopyranoside 4 was first isolated from a natural source, together with three known compounds, the ferulic acid heptyl ester 1, naringenin 2, and 4,2',4'-trihydroxy-6'-methoxychalcone-4'-O-β-d-glucopyranoside 3, which were isolated from peach [Prunus persica (L.) Batsch] fruits. These compounds were subjected to different virtual screening strategies in order to examine their activity to combat the COVID-19 outbreak. The study design composed of some major aspects: (a) docking with main protease (Mpro), (b) docking with spike protein, (c) 3D shape similarity study (Rapid Overlay Chemical Similarity-ROCS) to the clinically used drugs in COVID-19 patients, and finally, (d) the rule of five and the estimated pre-ADMT properties of the separated flavonoids. Docking study with Mpro of SARS-CoV-2 (PDB ID:6LU7, and 6Y2F) showed that compound 3, its aglycone part, and compound 4 have a strong binding mode to a protease receptor with key amino acids, especially Gln:166AA, and having a similar docking pose to co-crystalized ligands. Docking with the spike protein of SARS-CoV-2 illustrated that compounds 3 and 4 have a good binding affinity to PDB ID:6VSB through the formation of HBs with Asp:467A and Asn:422A. According to ROCS analysis, compounds 1, 3, and 4 displayed high similarities to drugs that prevent SARS-Co2 entry to the lung cells or block the inflammatory storm causing lung injury. Compounds 3 and 4 are good candidates for drug development especially because they showed predicted activity against SARS-CoV-2 through different mechanisms either by preventing genome replication or by blocking inflammatory storm that trigger lung injury. These compounds were isolated from peach fruit, and the study supports data and continues with the recommendation of peach fruits in controlling and managing COVID-19 cases.

A Network Pharmacology Approach to Explore the Mechanisms of Shugan Jianpi Formula in Liver Fibrosis

Purpose

We explored the mechanism of Shugan Jianpi Formula (SGJPF) and its effective components for the treatment of liver fibrosis (LF).

Materials and Methods

We collected the active ingredients in SGJPF through the Traditional Chinese Medicine Systems Pharmacology Database and Analysis Platform and screened the effective components by absorption, distribution, metabolism, and excretion. Herb-associated target proteins were predicted and screened based on the Bioinformatics Analysis Tool for Molecular Mechanism of Traditional Chinese Medicine and Search Tool for Interactions of Chemicals databases. LF-associated target proteins were predicted and screened based on the Online Mendelian Inheritance in Man® Database and Comparative Toxicogenomics Database. Common genes with LF and herbs were selected, and Cytoscape 3.5.1 software was used to construct an herb pathway and component-LF common target network. The Search Tool for the Retrieval of Interacting Genes/Proteins was used to build a protein-protein interaction, and quantitative PCR was used to verify the related target genes. Finally, clusterProfiler was applied for the analysis of Gene Ontology terms and Kyoto Encyclopedia of Genes and Genomes pathways.

Results

The pharmacological network contained 252 active compounds (e.g., Astragaloside A, saikosaponin, linoleic acid, and Poria acid A), 84 common target genes, and 94 significant signaling pathways. Among them, interleukin 6 (IL-6), tumor protein 53 p53 (TP53), prostaglandin-endoperoxide synthase 2 (PTGS2), AKT1, IL-1β, and the nucleotide-binding and oligomerization domain-like receptor and Janus kinase-signal transducer and activator of transcription signaling pathways were selected as the critical target gene and critical signal pathway, respectively.

Conclusion

The mechanisms of SGJPF in protecting against LF include the regulation of multiple targets such as IL-6, TP53, PTGS2, and AKT1. These target proteins affect LF through various signal transduction pathways.

Network pharmacology to dissect the mechanisms of Yinlai Decoction for pneumonia

BACKGROUND

Pneumonia is a common respiratory disorder, which brings an enormous financial burden to the medical system. However, the current treatment options for pneumonia are limited because of drug resistance and side effects. Our previous study preliminarily confirmed that Yinlai Decoction (YD), a common prescription for pneumonia in clinical practice, can regulate the expression of inflammatory factors, but the mechanisms are unknown yet.

METHODS

In our work, a method named network pharmacology was applied, which investigated the underlying mechanisms of herbs based on a variety of databases. We obtained bioactive ingredients of YD on TCMSP database and collected potential targets of these ingredients by target fishing. Then the pneumonia-related targets database was built by TTD, Drugbank, HPO, OMIM, and CTD. Based on the matching targets between YD and pneumonia, the PPI network was built by STRING to analyze the interactions among these targets and then input into Cytoscape for further topological analysis. DAVID and KEGG were utilized for GO and pathway enrichment analysis. Then rat model based on LPS stimulated pneumonia was used to verify the possible mechanism of YD in treating pneumonia.

RESULTS

Sixty-eight active ingredients, 103 potential targets and 8 related pathways, which likely exert a number of effects, were identified. Three networks were constructed using Cytoscape, which were herb-component-network, YD-pneumonia target network, and herb-component-YD target-pneumonia network. YD was verified to treat LPS-induced pneumonia by regulating the inflammatory factor IL-6, which was a predicted target.

CONCLUSION

Network analysis indicated that YD could alleviate the symptoms and signs of pneumonia through regulating host immune inflammatory response, angiogenesis and vascular permeability, the barrier function of the airway epithelial cells, hormone releasing and cell growth, proliferation, and apoptosis.

Facts, Patterns, and Principles in Drug Discovery: Appraising the Rule of 5 with Measured Physicochemical Data

The rule of 5 was designed to estimate the likelihood of poor absorption or permeation, noting the impact of poor solubility. This Perspective explores the impact of various physicochemical descriptors and contemporary lipophilicity measurements on permeability and solubility, showing that the distribution coefficient log D_7.4 (rather than log P) is the most impactful parameter. Molecular weight, almost invariably the defining characteristic of "beyond the rule of 5" compounds, has little impact on solubility when log D_7.4 measurements and aromaticity are considered. Predicting permeation is more complex, given passive and carrier transport mechanisms; however, notable patterns of behavior are apparent, giving insight even "beyond the rule of 5". Recommended best practices should involve using the facts (measurements) and the patterns they reveal to establish informative principles rather than fastidious rules.

Design, synthesis, and evaluation of compounds capable of reducing Pseudomonas aeruginosa virulence

Anti-virulence approaches in the treatment of Pseudomonas aeruginosa (PA)-induced infections have shown clinical potential in multiple in vitro and in vivo studies. However, development of these compounds is limited by several factors, including the lack of molecules capable of penetrating the membrane of gram-negative organisms. Here, we report the identification of novel structurally diverse compounds that inhibit PqsR and LasR-based signaling and diminish virulence factor production and biofilm growth in two clinically relevant strains of P. aeruginosa. It is the first report where potential anti-virulent agents were evaluated for inhibition of several virulence factors of PA. Finally, co-treatment with these inhibitors significantly reduced the production of virulence factors induced by the presence of sub-inhibitory levels of ciprofloxacin. Further, we have analyzed the drug-likeness profile of designed compounds using quantitative estimates of drug-likeness (QED) and confirmed their potential as hit molecules for further development.

Fraction Lipophilicity Index (FLI). A drug-like metric for orally administered ionizable drugs

Fraction Lipophilicity Index (FLI) was developed as a metric for assessing drug likeness of ionizable oral drugs. Considering that both log P and log D have distinct roles in drug action, the metric FLI allocates lipophilicity to a pH dependent neutral fraction of the molecule and is a weighted combination of log P and log D. It is expressed by equation: FLI = 2 x log P-│log D│. A dataset of 368 basic and acidic drugs was analyzed. Based on available % absorption data, drugs were classified into class 1 (268 drugs) and class 2 (100 drugs). The freeware MedChem Designer was used for log P and log D calculations at pH 7.4 and pH 5.5 for acids. Based on class 1, a drug-like FLI range 0-8 was defined. FLI distribution for class 2 is shifted towards significantly lower values. Comparison of FLI with rule of 5 (Ro5) shows that it leads to fewer values outside the established range than the corresponding log P violations for class 1. For class 2 it gives more alerts than Ro5 and can be considered complementary to Ro5, while it also sets lower limits to discriminate drugs with poor absorption.

Principles for targeting RNA with drug-like small molecules

Recent studies have indicated the potential to develop small-molecule drugs that act on RNA targets, leading to burgeoning interest in the field. This article discusses general principles for discovering small-molecule drugs that target RNA and argues that the overarching challenge is to identify appropriate target structures in disease-causing RNAs that have high information content and, consequently, appropriate ligand-binding pockets.

RNA molecules are essential for cellular information transfer and gene regulation, and RNAs have been implicated in many human diseases. Messenger and non-coding RNAs contain highly structured elements, and evidence suggests that many of these structures are important for function. Targeting these RNAs with small molecules offers opportunities to therapeutically modulate numerous cellular processes, including those linked to 'undruggable' protein targets. Despite this promise, there is currently only a single class of human-designed small molecules that target RNA used clinically — the linezolid antibiotics. However, a growing number of small-molecule RNA ligands are being identified, leading to burgeoning interest in the field. Here, we discuss principles for discovering small-molecule drugs that target RNA and argue that the overarching challenge is to identify appropriate target structures — namely, in disease-causing RNAs that have high information content and, consequently, appropriate ligand-binding pockets. If focus is placed on such druggable binding sites in RNA, extensive knowledge of the typical physicochemical properties of drug-like small molecules could then enable small-molecule drug discovery for RNA targets to become (only) roughly as difficult as for protein targets.

Present drug-likeness filters in medicinal chemistry during the hit and lead optimization process: how far can they be simplified?

During the past decade, decreasing the attrition rate of drug development candidates reaching the market has become one of the major challenges in pharmaceutical research and drug development (R&D). To facilitate the decision-making process, and to increase the probability of rapidly finding and developing high-quality compounds, a variety of multiparametric guidelines, also known as rules and ligand efficiency (LE) metrics, have been developed. However, what are the 'best' descriptors and how far can we simplify these drug-likeness prediction tools in terms of the numerous, complex properties that they relate to?

Discovery of potent 2,4-difluoro-linker poly(ADP-ribose) polymerase 1 inhibitors with enhanced water solubility and in vivo anticancer efficacy

Poly (ADP-ribose) polymerase 1 (PARP1) is overexpressed in a variety of cancers, especially in breast and ovarian cancers; tumor cells that are deficient in breast cancer gene 1/2 (BRCA1/2) are highly sensitive to PARP1 inhibition. In this study, we identified a series of 2,4-difluorophenyl-linker analogs (15-55) derived from olaparib as novel PARP1 inhibitors. Four potent analogs 17, 43, 47, and 50 (IC₅₀=2.2-4.4 nmol/L) effectively inhibited the proliferation of Chinese hamster lung fibroblast V-C8 cells (IC₅₀=3.2-37.6 nmol/L) in vitro, and showed specificity toward BRCA-deficient cells (SI=40-510). The corresponding hydrochloride salts 56 and 57 (based on 43 and 47) were highly water soluble in pH=1.0 buffered salt solutions (1628.2 μg/mL, 2652.5 μg/mL). In a BRCA1-mutated xenograft model, oral administration of compound 56 (30 mg·kg^-1·d^-1, for 21 d) exhibited more prominent tumor growth inhibition (96.6%) compared with the same dose of olaparib (56.3%); in a BRCA2-mutated xenograft model, oral administration of analog 43 (10 mg·kg^-1·d^-1, for 28 d) significantly inhibited tumor growth (69.0%) and had no negative effects on the body weights. Additionally, compound 56 exhibited good oral bioavailability (F=32.2%), similar to that of olaparib (F=45.4%). Furthermore, the free base 43 of the hydrochloride salt 56 exhibited minimal hERG inhibition activity (IC₅₀=6.64 μmol/L). Collectively, these data demonstrate that compound 56 may be an excellent drug candidate for the treatment of cancer, particularly BRCA-deficient tumors.

Statistical Profiling of One Promiscuous Protein Binding Site: Illustrated by Urokinase Catalytic Domain

While recent literature focuses on drug promiscuity, the characterization of promiscuous binding sites (ability to bind several ligands) remains to be explored. Here, we present a proteochemometric modeling approach to analyze diverse ligands and corresponding multiple binding sub-pockets associated with one promiscuous binding site to characterize protein-ligand recognition. We analyze both geometrical and physicochemical profile correspondences. This approach was applied to examine the well-studied druggable urokinase catalytic domain inhibitor binding site, which results in a large number of complex structures bound to various ligands. This approach emphasizes the importance of jointly characterizing pocket and ligand spaces to explore the impact of ligand diversity on sub-pocket properties and to establish their main profile correspondences. This work supports an interest in mining available 3D holo structures associated with a promiscuous binding site to explore its main protein-ligand recognition tendency.

A New Strategy for Deleting Animal drugs from Traditional Chinese Medicines based on Modified Yimusake Formula

Traditional Chinese medicine (TCM), such as Uyghur Medicine (UM) has been used in clinical treatment for many years. TCM is featured as multiple targets and complex mechanisms of action, which is normally a combination of medicinal herbs and sometimes even contains certain rare animal medicinal ingredients. A question arises as to whether these animal materials can be removed replaced from TCM applications due to their valuable rare resources or animal ethics. Here, we select a classical UM Yimusake formula, which contains 3 animal drugs and other 8 herbs, and has got wealthy experience and remarkable achievements in treating erectile dysfunction (ED) in China. The active components, drug targets and therapeutic mechanisms have been comprehensively analyzed by systems-pharmacology methods. Additionally, to validate the inhibitory effects of all candidate compounds on their related targets, in vitro experiments, computational analysis and molecular dynamics simulations were performed. The results show that the modified, original and three animal materials display very similar mechanisms for an effective treatment of ED, indicating that it is quite possible to remove these three animal drugs from the original formula while still keep its efficiency. This work provides a new attempt for deleting animal materials from TCM, which should be important for optimization of traditional medicines.

Computational analysis of calculated physicochemical and ADMET properties of protein-protein interaction inhibitors

The modulation of PPIs by low molecular weight chemical compounds, particularly by orally bioavailable molecules, would be very valuable in numerous disease indications. However, it is known that PPI inhibitors (iPPIs) tend to have properties that are linked to poor Absorption, Distribution, Metabolism, Excretion and Toxicity (ADMET) and in some cases to poor clinical outcomes. Previously reported in silico analyses of iPPIs have essentially focused on physicochemical properties but several other ADMET parameters would be important to assess. In order to gain new insights into the ADMET properties of iPPIs, computations were carried out on eight datasets collected from several databases. These datasets involve compounds targeting enzymes, GPCRs, ion channels, nuclear receptors, allosteric modulators, oral marketed drugs, oral natural product-derived marketed drugs and iPPIs. Several trends are reported that should assist the design and optimization of future PPI inhibitors, either for drug discovery endeavors or for chemical biology projects.

Global vision of druggability issues: applications and perspectives

During the preliminary stage of a drug discovery project, the lack of druggability information and poor target selection are the main causes of frequent failures. Elaborating on accurate computational druggability prediction methods is a requirement for prioritizing target selection, designing new drugs and avoiding side effects. In this review, we describe a survey of recently reported druggability prediction methods mainly based on networks, statistical pocket druggability predictions and virtual screening. An application for a frequent mutation of p53 tumor suppressor is presented, illustrating the complementarity of druggability prediction approaches, the remaining challenges and potential new drug development perspectives.

Compound high-quality criteria: a new vision to guide the development of drugs, current situation

For several decades, the pharmaceutical industry has suffered due to major issues such as reductions of the number of FDA approved drugs and biologics. Several analyses have been highlighted that the 'druglikeness' is one of the strategies to improve succeed rates of screening such as, for instance, high-throughput screening (HTS), and then hits (as starting point), leads and clinical candidates. It is clear that the improvement of compound quality accelerates the drug discovery projects. The monitoring of several indices to avoid 'molecular obesity' (ADMET problems) of final drugs from good-quality 'low-fat' starting points represents today a powerful strategy of optimization process. The development of the new guides to find drugs highlighting attempts at improving the attrition rate from hits to final medicines by focusing on how to improve the druggability of hits, leads and drugs during the drug discovery process represents a key approach to design next better generation of medicines.

Profiling the estimated plasma concentrations of 215 marketed oral drugs

The human pharmacokinetic parameters of 215 marketed oral drugs have been collated and their estimated plasma concentrations (following repeat dosing) profiled against time using a one-compartment model.

A new paradigm for navigating compound property related drug attrition

Improving the efficiency of drug discovery remains a major focus for the pharmaceutical industry. Toxicity accounts for 90% of withdrawals and major early-stage terminations relate to suboptimal efficacy and safety. Traditional oral drug space is well defined with respect to physicochemical properties and ADMET risks but increased focus on ligand-lipophilicity efficiency, maximizing enthalpy contributions and new target classes challenge this paradigm. A hybrid space has been identified that combines physical properties and key interactions attributable to drug transporters. A novel algorithm is proposed that incorporates drug-transporter interactions and its utility evaluated against popular ligand efficiency indices. Simply reducing the bulk properties of compounds can exchange one problem for another and creates high-risk areas that challenge the successful delivery from a balanced portfolio.

Applications of Fluorine in Medicinal Chemistry

The role of fluorine in drug design and development is expanding rapidly as we learn more about the unique properties associated with this unusual element and how to deploy it with greater sophistication. The judicious introduction of fluorine into a molecule can productively influence conformation, pKa, intrinsic potency, membrane permeability, metabolic pathways, and pharmacokinetic properties. In addition, (18)F has been established as a useful positron emitting isotope for use with in vivo imaging technology that potentially has extensive application in drug discovery and development, often limited only by convenient synthetic accessibility to labeled compounds. The wide ranging applications of fluorine in drug design are providing a strong stimulus for the development of new synthetic methodologies that allow more facile access to a wide range of fluorinated compounds. In this review, we provide an update on the effects of the strategic incorporation of fluorine in drug molecules and applications in positron emission tomography.

The application of in silico drug-likeness predictions in pharmaceutical research

The concept of drug-likeness, established from the analyses of the physiochemical properties or/and structural features of existing small organic drugs or/and drug candidates, has been widely used to filter out compounds with undesirable properties, especially poor ADMET (absorption, distribution, metabolism, excretion, and toxicity) profiles. Here, we summarize various approaches for drug-likeness evaluations, including simple rules/filters based on molecular properties/structures and quantitative prediction models based on sophisticated machine learning methods, and provide a comprehensive review of recent advances in this field. Moreover, the strengths and weaknesses of these approaches are briefly outlined. Finally, the drug-likeness analyses of natural products and traditional Chinese medicines (TCM) are discussed.

Lead optimization attrition analysis (LOAA): a novel and general methodology for medicinal chemistry

Herein, we report a novel and general method, lead optimization attrition analysis (LOAA), to benchmark two distinct small-molecule lead series using a relatively unbiased, simple technique and commercially available software. We illustrate this approach with data collected during lead optimization of two independent oncology programs as a case study. Easily generated graphics and attrition curves enabled us to calibrate progress and support go/no go decisions on each program. We believe that this data-driven technique could be used broadly by medicinal chemists and management to guide strategic decisions during drug discovery.

Advancing drug discovery: a pharmaceutics perspective

Current industry perspective of how discovery is conducted seems to be fragmented and does not have a unified overall outlook of how discovery challenges are being addressed. Consequently, well-defined processes and drug-likeness criteria are being viewed as "broken" and will not maintain future R&D productivity. In this commentary, an analysis of existing practices for defining successful development candidates resulted in a 5 "must do" list to help advance Drug Discovery as presented from a Pharmaceutics perspective. The 5 "must do" list includes: what an ideal discovery team model should look like, what criteria should be considered for the desired development candidate profile, what the building blocks of the development candidate should look like, and how to assess the development risks of the candidate.