Sulfotransferase-mediated phase II drug metabolism prediction of substrates and sites using accessibility and reactivity-based algorithms.

Sulphotransferases (SULTs) are a major phase II metabolic enzyme class contributing ~20 % to the Phase II metabolism of FDA-approved drugs. Ignoring the potential for SULT-mediated metabolism leaves a strong potential for drug-drug interactions, often causing late-stage drug discovery failures or black-boxed warnings on FDA labels. The existing models use only accessibility descriptors and machine learning (ML) methods for class and site of sulfonation (SOS) predictions for SULT. In this study, a variety of accessibility, reactivity, and hybrid models and algorithms have been developed to make accurate substrate and SOS predictions. Unlike the literature models, reactivity parameters for the aliphatic or aromatic hydroxyl groups (R/Ar-O-H), the Bond Dissociation Energy (BDE) gave accurate models with a True Positive Rate (TPR)=0.84 for SOS predictions. We offer mechanistic insights to explain these novel findings that are not recognized in the literature. The accessibility parameters like the ratio of Chemgauss4 Score (CGS) and Molecular Weight (MW) CGS/MW and distance from cofactor (Dis) were essential for class predictions and showed TPR=0.72. Substrates consistently had lower BDE, Dis, and CGS/MW than non-substrates. Hybrid models also performed acceptablely for SOS predictions. Using the best models, Algorithms gave an acceptable performance in class prediction: TPR=0.62, False Positive Rate (FPR)=0.24, Balanced accuracy (BA)=0.69, and SOS prediction: TPR=0.98, FPR=0.60, and BA=0.69. A rule-based method was added to improve the predictive performance, which improved the algorithm TPR, FPR, and BA. Validation using an external dataset of drug-like compounds gave class prediction: TPR=0.67, FPR=0.00, and SOS prediction: TPR=0.80 and FPR=0.44 for the best Algorithm. Comparisons with standard ML models also show that our algorithm shows higher predictive performance for classification on external datasets. Overall, these models and algorithms (SOS predictor) give accurate substrate class and site (SOS) predictions for SULT-mediated Phase II metabolism and will be valuable to the drug discovery community in academia and industry. The SOS predictor is freely available for academic/non-profit research via the GitHub link.

Non-animal models for blood-brain barrier permeability evaluation of drug-like compounds.

Diseases related to the central nervous system (CNS) are major health concerns and have serious social and economic impacts. Developing new drugs for CNS-related disorders presents a major challenge as it actively involves delivering drugs into the CNS. Therefore, it is imperative to develop in silico methodologies to reliably identify potential lead compounds that can penetrate the blood-brain barrier (BBB) and help to thoroughly understand the role of different physicochemical properties fundamental to the BBB permeation of molecules. In this study, we have analysed the chemical space of the CNS drugs and compared it to the non-CNS-approved drugs. Additionally, we have collected a feature selection dataset from Muehlbacher et al. (J Comput Aided Mol Des 25(12):1095-1106, 2011. 10.1007/s10822-011-9478-1) and an in-house dataset. This information was utilised to design a molecular fingerprint that was used to train machine learning (ML) models. The best-performing models reported in this study achieved accuracies of 0.997 and 0.98, sensitivities of 1.0 and 0.992, specificities of 0.971 and 0.962, MCCs of 0.984 and 0.958, and ROC-AUCs of 0.997 and 0.999 on an imbalanced and a balanced dataset, respectively. They demonstrated overall good accuracies and sensitivities in the blind validation dataset. The reported models can be applied for fast and early screening drug-like molecules with BBB potential. Furthermore, the bbbPythoN package can be used by the research community to both produce the BBB-specific molecular fingerprints and employ the models mentioned earlier for BBB-permeability prediction.

Improved QSAR models for PARP-1 inhibition using data balancing, interpretable machine learning, and matched molecular pair analysis.

The poly (ADP-ribose) polymerase-1 (PARP-1) enzyme is an important target in the treatment of breast cancer. Currently, treatment options include the drugs Olaparib, Niraparib, Rucaparib, and Talazoparib; however, these drugs can cause severe side effects including hematological toxicity and cardiotoxicity. Although in silico models for the prediction of PARP-1 activity have been developed, the drawbacks of these models include low specificity, a narrow applicability domain, and a lack of interpretability. To address these issues, a comprehensive machine learning (ML)-based quantitative structure-activity relationship (QSAR) approach for the informed prediction of PARP-1 activity is presented. Classification models built using the Synthetic Minority Oversampling Technique (SMOTE) for data balancing gave robust and predictive models based on the K-nearest neighbor algorithm (accuracy 0.86, sensitivity 0.88, specificity 0.80). Regression models were built on structurally congeneric datasets, with the models for the phthalazinone class and fused cyclic compounds giving the best performance. In accordance with the Organization for Economic Cooperation and Development (OECD) guidelines, a mechanistic interpretation is proposed using the Shapley Additive Explanations (SHAP) to identify the important topological features to differentiate between PARP-1 actives and inactives. Moreover, an analysis of the PARP-1 dataset revealed the prevalence of activity cliffs, which possibly negatively impacts the model's predictive performance. Finally, a set of chemical transformation rules were extracted using the matched molecular pair analysis (MMPA) which provided mechanistic insights and can guide medicinal chemists in the design of novel PARP-1 inhibitors.

In vitro, in-vivo, and in-silico investigation of physicochemical interactions between pioglitazone and rifampicin.

There is a possibility of in-situ physicochemical interactions between concomitantly administered drugs. This study aimed to investigate such physicochemical interactions between pioglitazone and rifampicin. Pioglitazone exhibited significantly higher dissolution in the presence of rifampicin, while the dissolution of rifampicin remained unaffected. The solid-state characterization of precipitates recovered after pH-shift dissolution experiments revealed the conversion of pioglitazone into an amorphous form in the presence of rifampicin. The Density Function Theory (DFT) calculations showed the intermolecular hydrogen bonding between rifampicin and pioglitazone. In-situ conversion of pioglitazone in amorphous form and subsequent supersaturation of GIT milieu translated into significantly higher in-vivo exposure of pioglitazone and its metabolites (M-III and M-IV) in Wistar rats. Therefore, it is advisable to consider the possibility of physicochemical interactions between concomitantly administered drugs. Our findings may be beneficial in tailoring the dose of concomitantly administered drugs, particularly for chronic conditions that entail polypharmacy.

An overview of compound properties, multiparameter optimization, and computational drug design methods for PARP-1 inhibitor drugs.

Breast cancer treatment with PARP-1 inhibitors remains challenging due to emerging toxicities, drug resistance, and unaffordable costs of treatment options. How do we invent strategies to design better anti-cancer drugs? A part of the answer is in optimized compound properties, desirability functions, and modern computational drug design methods that drive selectivity and toxicity and have not been reviewed for PARP-1 inhibitors. Nonetheless, comparisons of these compound properties for PARP-1 inhibitors are not available in the literature. In this review, we analyze the physchem, PKPD space to identify inherent desirability functions characteristic of approved drugs that can be valuable for the design of better candidates. Recent literature utilizing ligand, structure-based drug design strategies and matched molecular pair analysis (MMPA) for the discovery of novel PARP-1 inhibitors are also reviewed. Thus, this perspective provides valuable insights into the medchem and multiparameter optimization of PARP-1 inhibitors that might be useful to other medicinal chemists.

Mechanisms of Electron Transfer Rate Modulations in Cytochrome P450 BM3.

Bacterial cytochromes P450 BM3 (CYP450 BM3) catalyze reactions of industrial importance. Despite many successful biotransformations, robust (re)design for novel applications remains challenging. Rational design and evolutionary approaches are not always successful, highlighting a lack of complete understanding of the mechanisms of electron transfer (ET) modulations. Thus, the full potential of CYP450 reactions remains under-exploited. In this work, we report the first molecular dynamics (MD)-based explicit prediction of BM3 ET parameters (reorganization energies; λ and ET free energies; ΔG°), and log ET rates (log kET) using the Marcus theory. Overall, the calculated ET rates for the BM3 wild-type (WT), mutants (F393 and L86), ligand-bound state, and ion concentrations agree well with experimental data. In ligand-free (LF) BM3, mutations modulate kET via ET ΔG°. Simulations show that the experimental ET rate enhancement is due to increased driving force (more negative ΔG°) upon ligation. This increase is related to the protein reorganization required to accommodate the ligand in the binding pocket rather than binding interactions with the ligand. Our methodology (CYPWare 1.0) automates all the stages of the MD simulation step-up, energy calculations, and estimation of ET parameters. CYPWare 1.0 and this work thus represent an important advancement in the CYP450 ET rate predictions, which has the potential to guide the redesign of ET enzymes. This program and a Web tool are available on GitHub for academic research.

Preparation and characterization of amoxapine- and naringin-loaded solid lipid nanoparticles: drug-release and molecular-docking studies.

Aim: Amoxapine (AMX) has been reported to be metabolized by CYP3A4 and CYP2D6. Naringin (NG) has been reported to inhibit CYP enzymes. Therefore, the current work was designed to develop AMX solid lipid nanoparticles (AMX-SLNs) and NG-SLNs for better therapeutic performance. Materials & methods: AMX-SLNs and NG-SLNs were prepared and characterized. AMX and NG interactions with CYP450s were studied with molecular docking to rationalize the effectiveness of the combination. Results: AMX-SLNs and NG-SLNs showed nanometric size with a sustained in vitro drug-release profile. NG showed a higher predicted binding affinity for CYP3A4 and CYP2D6, suggesting the potential for inhibition. Conclusion: The developed formulations were thoroughly characterized along with molecular docking data indicating promising AMX and NG combinations that may show good therapeutic activity.

Methemoglobin formation in mutant hemoglobin α chains: electron transfer parameters and rates.

Hemoglobin-mediated transport of dioxygen (O2) critically depends on the stability of the reduced (Fe2+) form of the heme cofactors. Some protein mutations stabilize the oxidized (Fe3+) state (methemoglobin, Hb M), causing methemoglobinemia, and can be lethal above 30%. The majority of the analyses of factors influencing Hb oxidation are retrospective and give insights only for inner-sphere mutations of heme (His58, His87). Herein, we report the first all-atom molecular dynamics simulations on both redox states and calculations of the Marcus electron transfer (ET) parameters for the α chain Hb oxidation and reduction rates for Hb M. The Hb wild-type (WT) and most of the studied α chain variants maintain globin structure except the Hb M Iwate (H87Y). The mutants forming Hb M tend to have lower redox potentials and thus stabilize the oxidized (Fe3+) state (in particular, the Hb Miyagi variant with K61E mutation). Solvent reorganization (λsolv 73-96%) makes major contributions to reorganization free energy, whereas protein reorganization (λprot) accounts for 27-30% except for the Miyagi and J-Buda variants (λprot ∼4%). Analysis of heme-solvent H-bonding interactions among variants provide insights into the role of Lys61 residue in stabilizing the Fe2+ state. Semiclassical Marcus ET theory-based calculations predict experimental kET for the Cyt b5-Hb complex and provide insights into relative reduction rates for Hb M in Hb variants. Thus, our methodology provides a rationale for the effect of mutations on the structure, stability, and Hb oxidation reduction rates and has potential for identification of mutations that result in methemoglobinemia.

A property-response perspective on modern toxicity assessment and drug toxicity index (DTI).

Toxicity related failures in drug discovery and clinical development have motivated scientists and regulators to develop a wide range of in-vitro, in-silico tools coupled with data science methods. Older drug discovery rules are being constantly modified to churn out any hidden predictive value. Nonetheless, the dose-response concepts remain central to all these methods. Over the last 2 decades medicinal chemists, and pharmacologists have observed that different physicochemical, and pharmacological properties capture trends in toxic responses. We propose that these observations should be viewed in a comprehensive property-response framework where dose is only a factor that modifies the inherent toxicity potential. We then introduce the recently proposed "Drug Toxicity Index (DTI)" and briefly summarize its applications. A webserver is available to calculate DTI values (https://all-tool-kit.github.io/Web-Tool.html).

How Do Metal Ions Modulate the Rate-Determining Electron-Transfer Step in Cytochrome P450 Reactions?

Cytochrome P450 (CYP450) enzymes play important roles in maintaining human health and their reaction rates are dependent on the first electron transfer from the reduction partner. Interestingly, experimental work has shown that this step is highly influenced by the addition of metal ions. To understand the effect of external perturbations on the CYP450 first reduction step, we have performed a computational study with model complexes in the presence of metal and organic ions, solvent molecules, and an electric field. The results show that these medium-range interactions affect the driving force as well as electron-transfer rates dramatically. Based on the location, distance, and direction of the ions/electric field, the catalytic reaction rates are enhanced or impaired. Calculations on a large crystal structure with bonded alkali metal ions indicated inhibition patterns of the ions. Therefore, we predict that the active forms of the natural CYP450 isozymes will not have more than one alkali metal ion bound in the second-coordination sphere. As such, this study provides an insight into the activity of CYP450 enzymes and the effects of ions and electric field perturbations on their activity.

A simple model to solve a complex drug toxicity problem.

Linear drug toxicity models like therapeutic index (TI), physicochemical rules (rule of five, 3/75), ligand efficiency indices (LEI), ideal pharmacokinetic (PK) and pharmacodynamic (PD) profiles are widely used in drug discovery and development. In spite of this, predicting drug toxicity at various stages remains challenging and the overall productivity (<20%) and ultimate benefit to the patients remain low. A simple drug toxicity model, "Drug Toxicity Index" (DTI), is developed here using 711 oral drugs. DTI redefines drug toxicity as scaled biphasic and exponential functions of PD, PK and physicochemical parameters. PD, PK and physicochemical toxicity contributions were estimated from the on and off target IC50, maximum unbound plasma drug concentration (free C max), and log D values, respectively. These contributions are then scaled by molar dose and oral bioavailability and the logarithm of the sum of scaled contributions is DTI. Drugs with DTI above the WHO ATC drug category specific average values consistently have toxic profiles, while drugs with DTI below this average are relatively safe. DTI performs better than standard rules for lead optimization, LEI and exposure based TIs in identifying safe and toxic drugs. DTI classifies 392 drugs reported in the US-FDA's Liver Toxicity Knowledge Base (LTKB) with an AUC for ROC curves of 0.91-0.64 for different WHO ATC categories. DTI has been used to predict network meta-analysis results on relative toxicity within/across eight different therapeutic areas. It is useful in understanding PD, PK and physicochemical toxicity contributions and identifying potentially toxic drugs and the toxicity of recently approved drugs. Decision trees are proposed for applying the DTI concept in preclinical drug discovery and clinical trial settings. DTI can potentially reduce failure in drug discovery and might be useful in therapeutic drug monitoring and in xenobiotic and environmental toxicity studies.

Zingiber officinale and 6-gingerol alleviate liver and kidney dysfunctions and oxidative stress induced by mercuric chloride in male rats: A protective approach.

Mercury toxicity is an emerging problem in the world as its concentration is rising continuously due to increased industrial, medicinal and domestic uses. Exposure to mercury represents a serious challenge to humans and other living biomes. The aim of the present study was to assess the protective effect of natural products as Zingiber officinale extract and its active compound (6-gingerol) against mercuric chloride-induced hepatorenal toxicity and oxidative stress in male rats. Male Sprague-Dawley rats (150±10g, n=6 per group) were administered HgCl2 (12µmol/kg, ip; once only) the treatment of Zingiber officinale Rosc. extract (ZO: 125mg/kg, po) and 6-gingerol (GG: 50mg/kg, po) for three days after 24h of HgCl2 administration. Acute HgCl2 administration altered various biochemical parameters, including transaminases, alkaline phosphatase, lactate dehydrogenase, bilirubin, gamma-glutamyl transferase, triglycerides and cholesterol, urea, creatinine, uric acid and blood urea nitrogen contents with a concomitant decline in protein and albumin concentration in serum. In addition, a significant rise in lipid peroxidation level with concomitant decrease in reduced glutathione content and the antioxidant enzymes activities of superoxide dismutase, catalase, glutathione reductase, glutathione peroxidase and glutathione-S-transferase after acute HgCl2 exposure. Results of the present investigation clearly showed that both treatments as Zingiber officinale extract and 6-gingerol provide protection against acute mercuric chloride-intoxication by preventing oxidative degradation of a biological membrane from metal mediated free radical attacks. Biochemical data were well supported by histopathological findings. In conclusion, natural products may be an ideal choice against oxidative damage induced by mercury poisoning.

Curcuma longa Linn. extract and curcumin protect CYP 2E1 enzymatic activity against mercuric chloride-induced hepatotoxicity and oxidative stress: A protective approach.

The present investigation has been conducted to evaluate the therapeutic potential of Curcuma longa (200mgkg-1, po) and curcumin (80mgkg-1, po) for their hepatoprotective efficacy against mercuric chloride (HgCl2: 12µmolkg-1, ip; once only) hepatotoxicity. The HgCl2 administration altered various biochemical parameters, including transaminases, alkaline phosphatase, lactate dehydrogenase, bilirubin, gamma-glutamyl transferase, triglycerides and cholesterol contents with a concomitant decline in protein and albumin concentration in serum which were restored towards control by therapy of Curcuma longa or curcumin. On the other hand, both treatments showed a protective effect on drug metabolizing enzymes viz. aniline hydroxylase (AH) and amidopyrine-N-demethylase (AND), hexobarbitone induced sleep time and BSP retention. Choleretic, 1,1-diphenyl-2-picryl-hydrazil (DPPH)-free radical scavenging activities and histological studies also supported the biochemical findings. The present study concludes that Curcuma longa extract or curcumin has the ability to alleviate the hepatotoxic effects caused by HgCl2 in rats.

Design and synthesis of novel Y-shaped barbituric acid derivatives as PPARγ activators.

Novel Y-shaped barbituric acid (BA) derivatives have been designed using rational methods including molecular docking. Fourteen novel compounds were synthesized using hydroxyl group protection-deprotection strategies for PPARγ activation. Competitive binding analysis of the synthesized molecules using time-resolved fluorescence resonance energy transfer (FRET) method was carried out, and the IC50 values were determined. The symmetrically substituted derivatives have shown greater binding affinity than unsymmetrically substituted derivatives. Nitrobenzyl and cyanophenyl substituted derivatives have shown reasonable binding affinities (10.1 and 6.5 µM, respectively), while mono and diacetate derivatives were found inactive. Molecular dynamics simulations show that the designed compounds have interaction profiles similar to partial agonists. The most significant finding of our study is that BA derivatives with symmetrically substituted weakly polar side chains result in the desired moderate level of PPARγ binding affinities.

Density functional study on the cytochrome-mediated S-oxidation: identification of crucial reactive intermediate on the metabolic path of thiazolidinediones.

S-Oxidation is an important cytochrome P450 (CYP450)-catalyzed reaction, and the structural and energetic details of this process can only be studied by using quantum chemical methods. Thiazolidinedione (TZD) ring metabolism involving initial S-oxidation leads to the generation of reactive metabolites (RMs) and subsequent toxicity forcing the withdrawal of the glitazone class of drugs, thus, the study of the biochemical pathway of TZD ring metabolism is a subject of interest. The S-oxidation of the TZD ring and the formation of the isocyanate intermediate (ISC) was implicated as a possible pathway; however, there are several questions still unanswered in this biochemical pathway. The current study focuses on the CYP450-mediated S-oxidation, fate of the sulfoxide product (TZDSO), ring cleavage to ISC, and formation of nucleophilic adducts. The process of S-oxidation was explored by using Cpd I (iron(IV)-oxo porphyrin, to mimic CYP450) at TZVP/6-311+G(d) basis set. The barriers were calculated after incorporating dispersion and solvent corrections. The metabolic conversion from TZDSO to ISC (studied at B3LYP/6-311++G(2df,3pd)//B3LYP/6-31+G(d)) required a novel protonated intermediate, TZDSOH(+). The effect of higher basis sets (6-311+G(d,p), aug-cc-pvqz) on this conversion was studied. TZDSOH(+) was observed to be more reactive and thermodynamically accessible than ISC, indicating that TZDSOH(+) is the actual reactive intermediate leading to toxicity of the TZD class of compounds.

Formation/fate of reactive metabolites from general anesthetics and a comparison of toxic and non-toxic analogues: a DFT study.

Chloroform and Halothane are well known hepatotoxic anesthetics for which toxicity is attributed to their reactive metabolites. The molecular level details of reactions leading to the formation of reactive metabolites from chloroform and halothane have not been explored. Potential energy surface (PES) for the formation of phosgene (a toxic intermediate) from Chloroform has been studied using quantum chemical methods. The HOOH mediated reaction of chloroform to give phosgene has been found to be exothermic by 81.24 kcal/mol with a barrier of ~ 3 kcal/mol through the water catalyzed transition sate. The quantum chemical studies on the reactivity profile of phosgene indicated that urea derivatives need to be considered on the mechanism leading to toxicity. Similarly, metabolic pathways of Halothane oxidation have been studied. The C-H bond dissociation energies (BDE) and radical stabilization energies (RSE) for Chloroform and Halothane (< 95 kcal/mol and > 10 kcal/mol) were found to be significantly different for these toxic anesthetics in comparison to their safer analogues (> 100 kcal/mol and < 5 kcal/mol) respectively; thus these parameters can be employed to distinguish toxic and non-toxic general anesthetics. Enthalpy for the Cpd I, a widely used model for CYP450 enzymes, mediated reactions also agreed well with these results.

Toxic metabolite formation from Troglitazone (TGZ): new insights from a DFT study.

The hepatotoxicity of Troglitazone (TGZ) has been ascribed to the formation of reactive metabolites, and the primary reactive metabolite of TGZ has been confirmed to be an o-quinone methide. Oxidation of the chromane moiety is also known to produce quinone containing metabolites. Quantum chemical studies have been performed to analyze the possible reaction pathways for the metabolism of the TGZ side chain, 6-hydroxy-2,2,5,7,8-pentamethylchromane (HPMC). From this analysis, a new pathway including oxidation at the C13 and C14 atoms of HPMC has been proposed for the formation of o-quinone methide (M2), while oxidation at the hydroxyl group leads to the formation of the quinone metabolite (M7). o-Quinone methide reactive metabolites have been shown to be more electrophilic at the reactive methylene center using quantum chemically estimated parameters.

A simple, mild, and regioselective method for the benzylation of carbohydrate derivatives promoted by silver carbonate.

A simple, mild, and regioselective method has been developed for the selective benzylation and p-methoxybenzylation of carbohydrate derivatives in high yields using Ag(2)CO(3) as the promoter. Benzylation of base-labile substrates, for which other reported methods are of little use, has been performed in high yields.