Prediction of drug disposition on the basis of its chemical structure

Structural Investigation of Diclofenac Binding to Ovine, Caprine, and Leporine Serum Albumins

Free drug concentration in the blood sera is crucial for its appropriate activity. Serum albumin, the universal blood carrier protein, is responsible for transporting drugs and releasing them into the bloodstream. Therefore, a drug's binding to SA is especially important for its bioavailability and it is a key problem in the drug design process. In this paper, we present crystal structures of three animal serum albumin complexes: ovine, caprine, and leporine, with diclofenac, a popular non-steroidal anti-inflammatory drug that is used in therapy of chronic and acute pain. Details of diclofenac binding mode by the presented serum albumins are compared with analogous complexes of human and equine serum albumins. The analysis of the occupied binding pockets in crystal structures of the investigated serum albumins from different mammals shows that they have two common and a number of unique diclofenac binding sites. The most intriguing is the fact that the albumins from the described species are able to bind different numbers of molecules of this popular anti-inflammatory drug, but none of the binding sites overlap with ones in the human serum albumin.

Computational medicinal chemistry role in clinical pharmacy education: Ingavirin for coronavirus disease 2019 (COVID-19) discovery model

Objective

Given the major shift to patient-directed education, novel coronavirus (nCoV) provides a live example on how medicinal chemistry could be a key science to teach pharmacy students. In this paper, students and clinical pharmacy practitioners will find a stepwise primer on identifying new potential nCoV treatments mechanistically modulated through angiotensin-converting enzyme 2 (ACE2).

Methods

First, we identified the maximum common pharmacophore between carnosine and melatonin as background ACE2 inhibitors. Second, we performed a similarity search to spot out structures containing the pharmacophore. Third, molinspiration bioactivity scoring enabled us to promote one of the newly identified molecules as the best next candidate for nCoV. Preliminary docking in SwissDock and visualization through University of California San Francisco (UCSF) chimera made it possible to qualify one of them for further detailed docking and experimental validation.

Results

Ingavirin had the best docking results with full fitness of -3347.15 kcal/mol and estimated ΔG of -8.53 kcal/mol compared with melatonin (-6.57 kcal/mol) and carnosine (-6.29 kcal/mol). UCSF chimera showed viral spike protein elements binding to ACE2 retained in the best ingavirin pose in SwissDock at 1.75 Angstroms.

Conclusion

Ingavirin has a promising inhibitory potential to host (ACE2 and nCoV spike protein) recognition, and hence could offer the next best mitigating effect against the current coronavirus disease (COVID-19) pandemic.

A Critical Review on Transplacental Transfer of Per- and Polyfluoroalkyl Substances: Prenatal Exposure Levels, Characteristics, and Mechanisms

Prenatal exposure to perfluoroalkyl and polyfluoroalkyl substances (PFASs) has aroused public concerns as it can pose multiple health threats to pregnant women and cause adverse birth outcomes for fetuses. In previous studies, the prenatal exposure levels and transplacental transfer efficiencies (TTE) of PFASs have been reported and discussed. Specifically, the binding affinities between PFASs and some transporters were determined, demonstrating that the TTE values of PFASs are highly dependent on their binding behaviors. To summarize primary findings of previous studies and propose potential guidance for future research, this article provides a systematic overview on levels and characteristics of prenatal exposure to PFASs worldwide, summarizes relationships between TTE values and structures of PFASs, and discusses possible transplacental transfer mechanisms, especially for the combination between PFASs and transporters. Given the critical roles of transporters in the transplacental transfer of PFASs, we conducted molecular docking to further clarify the binding behaviors between PFASs and the selected transporters. We proposed that the machine learning can be a superior method to predict and reveal behaviors and mechanisms of the transplacental transfer of PFASs. In total, this is the first review providing a comprehensive overview on the prenatal exposure levels and transplacental transfer mechanisms of PFASs.

Optimization of Personalized Amlodipine Dosing Strategies for Children Based on Pharmacokinetic Data from Chinese Male Adults and PBPK Modeling

For children, a special population who are continuously developing, a reasonable dosing strategy is the key to clinical therapy. Accurate dose predictions can help maximize efficacy and minimize pain in pediatrics. Methods: This study collected amlodipine pharmacokinetics (PK) data from 236 Chinese male adults and established a physiological pharmacokinetic (PBPK) model for adults using GastroPlus™. A PBPK model of pediatrics is constructed based on hepatic-to-body size and enzyme metabolism, used similar to the AUC_0-∞ to deduce the optimal dosage of amlodipine for children aged 1–16 years. A curve of continuous administration for 2-, 6-, 12-, 16-, and 25-year-olds and a personalized administration program for 6-year-olds were developed. Results: The results show that children could not establish uniform allometric amplification rules. The optimal doses were 0.10 mg·kg⁻¹ for ages 2–6 years and −0.0028 × Age + 0.1148 (mg/kg) for ages 7–16 years, r = 0.9941. The trend for continuous administration was consistent among different groups. In a 6-year-old child, a maintenance dose of 2.30 mg was used to increase the initial dose by 2.00 mg and the treatment dose by 1.00 mg to maintain stable plasma concentrations. Conclusions: A PBPK model based on enzyme metabolism can accurately predict the changes in the pharmacokinetic parameters of amlodipine in pediatrics. It can be used to support the optimization of clinical treatment plans in pediatrics.

Biomimetic Chromatographic Studies Combined with the Computational Approach to Investigate the Ability of Triterpenoid Saponins of Plant Origin to Cross the Blood-Brain Barrier

Biomimetic (non-cell based in vitro) and computational (in silico) studies are commonly used as screening tests in laboratory practice in the first stages of an experiment on biologically active compounds (potential drugs) and constitute an important step in the research on the drug design process. The main aim of this study was to evaluate the ability of triterpenoid saponins of plant origin to cross the blood-brain barrier (BBB) using both computational methods, including QSAR methodology, and biomimetic chromatographic methods, i.e., High Performance Liquid Chromatography (HPLC) with Immobilized Artificial Membrane (IAM) and cholesterol (CHOL) stationary phases, as well as Bio-partitioning Micellar Chromatography (BMC). The tested compounds were as follows: arjunic acid (Terminalia arjuna), akebia saponin D (Akebia quinata), bacoside A (Bacopa monnieri) and platycodin D (Platycodon grandiflorum). The pharmacokinetic BBB parameters calculated in silico show that three of the four substances, i.e., arjunic acid, akebia saponin D, and bacoside A exhibit similar values of brain/plasma equilibration rate expressed as logPSFubrain (the average logPSFubrain: -5.03), whereas the logPSFubrain value for platycodin D is -9.0. Platycodin D also shows the highest value of the unbound fraction in the brain obtained using the examined compounds (0.98). In these studies, it was found out for the first time that the logarithm of the analyte-micelle association constant (logKMA) calculated based on Foley's equation can describe the passage of substances through the BBB. The most similar logBB values were obtained for hydrophilic platycodin D, applying both biomimetic and computational methods. All of the obtained logBB values and physicochemical parameters of the molecule indicate that platycodin D does not cross the BBB (the average logBB: -1.681), even though the in silico estimated value of the fraction unbound in plasma is relatively high (0.52). As far as it is known, this is the first paper that shows the applicability of biomimetic chromatographic methods in predicting the penetration of triterpenoid saponins through the BBB.

Carnosine to Combat Novel Coronavirus (nCoV): Molecular Docking and Modeling to Cocrystallized Host Angiotensin-Converting Enzyme 2 (ACE2) and Viral Spike Protein

Aims: Angiotensin-converting enzyme 2 (ACE2) plays an important role in the entry of coronaviruses into host cells. The current paper described how carnosine, a naturally occurring supplement, can be an effective drug candidate for coronavirus disease (COVID-19) on the basis of molecular docking and modeling to host ACE2 cocrystallized with nCoV spike protein. Methods: First, the starting point was ACE2 inhibitors and their structure–activity relationship (SAR). Next, chemical similarity (or diversity) and PubMed searches made it possible to repurpose and assess approved or experimental drugs for COVID-19. Parallel, at all stages, the authors performed bioactivity scoring to assess potential repurposed inhibitors at ACE2. Finally, investigators performed molecular docking and modeling of the identified drug candidate to host ACE2 with nCoV spike protein. Results: Carnosine emerged as the best-known drug candidate to match ACE2 inhibitor structure. Preliminary docking was more optimal to ACE2 than the known typical angiotensin-converting enzyme 1 (ACE1) inhibitor (enalapril) and quite comparable to known or presumed ACE2 inhibitors. Viral spike protein elements binding to ACE2 were retained in the best carnosine pose in SwissDock at 1.75 Angstroms. Out of the three main areas of attachment expected to the protein–protein structure, carnosine bound with higher affinity to two compared to the known ACE2 active site. LibDock score was 92.40 for site 3, 90.88 for site 1, and inside the active site 85.49. Conclusion: Carnosine has promising inhibitory interactions with host ACE2 and nCoV spike protein and hence could offer a potential mitigating effect against the current COVID-19 pandemic.

Prolonged oral transmucosal delivery of highly lipophilic drug cannabidiol

Delivery of drugs through oral mucosa enables bypass of the gastrointestinal tract and "first pass" metabolism in the liver and the gut. Thus, a higher and less variable bioavailability can be obtained. Mechanisms of this administration route for cannabidiol were investigated in the current research in pigs. Results show that cannabidiol has substantially low permeability rate over 8 h through oral mucosa and accumulates significantly within it. Furthermore, following the removal of the delivery device, residual prolongation of release from the oral mucosa into systemic blood circulation continues for several hours. This method of delivery enabled acquisition of clinically relevant plasma levels of cannabidiol. The absorption profile indicates that cannabidiol, as well as other lipophilic molecules, should be delivered through oral mucosa for systemic absorption from a device that conceals the drug and prevents its washout by the saliva flow and subsequent ingestion into gastrointestinal tract.

Terpenoids: shape and non-covalent interactions. The rotational spectrum of cis-verbenol and its 1 : 1 water complex

In isolated and mono-hydrated verbenol, as in simpler allyl alcohols, the conformational leading force is the OH⋯π interaction.

Evaluation of drug efficacy based on the spatial position comparison of drug–target interaction centers

The spatial position and interaction of drugs and their targets is the most important characteristics for understanding a drug’s pharmacological effect, and it could help both in finding new and more precise treatment targets for diseases and in exploring the targeting effects of the new drugs. In this work, we develop a computational pipeline to confirm the spatial interaction relationship of the drugs and their targets and compare the drugs’ efficacies based on the interaction centers. First, we produce a 100-sample set to reconstruct a stable docking model of the confirmed drug–target pairs. Second, we set 5.5 Å as the maximum distance threshold for the drug–amino acid residue atom interaction and construct 3-dimensional interaction surface models. Third, by calculating the spatial position of the 3-dimensional interaction surface center, we develop a comparison strategy for estimating the efficacy of different drug–target pairs. For the 1199 drug–target interactions of the 649 drugs and 355 targets, the drugs that have similar interaction center positions tend to have similar efficacies in disease treatment, especially in the analysis of the 37 targeted relationships between the 15 known anti-cancer drugs and 10 target molecules. Furthermore, the analysis of the unpaired anti-cancer drug and target molecules suggests that there is a potential application for discovering new drug actions using the sampling molecular docking and analyzing method. The comparison of the drug–target interaction center spatial position method better reflect the drug–target interaction situations and could support the discovery of new efficacies among the known anti-cancer drugs.

A framework for application of quantitative property-property relationships (QPPRs) in physiologically based pharmacokinetic (PBPK) models for high-throughput prediction of internal dose of inhaled organic chemicals

New generation of toxicological tests and assessment strategies require validated toxicokinetic data or models that are lacking for most chemicals. This study aimed at developing a quantitative property-property relationship (QPPR)-based human physiologically based pharmacokinetic (PBPK) modeling framework for high-throughput predictions of inhalation toxicokinetics of organic chemicals. A PBPK model was parameterized with QPPR-derived values for hepatic clearance (CL_h) and partition coefficients (P) [blood:air (P_ba) and tissue:air (P_ta) and tissue:blood (P_tb)]. The model was initially applied to an evaluation dataset of 40 organic chemicals in the applicability domain, and then to an expanded dataset of 249 organic chemicals from diverse chemical classes. 'Batch' analyses were performed for rapid assessments of hundreds of chemicals. The simulations of inhalation toxicokinetics following an 8-h exposure to 1 ppm of each chemical were successful. The mean ratios of their predicted-to-experimental values were within a factor of 1.36-2.36 for P_tb and 1.18 for CL_h, for 80% of the chemicals in the evaluation dataset. The predicted 24-h area under the venous blood concentration-time curve (AUC₂₄) values were within the predicted envelopes obtained while using experimental values of P_ba and considering either no or maximal hepatic extraction. The reliability analysis (based on combined sensitivity and uncertainty analyses) indicated that AUC₂₄ predictions for 55% of the expanded dataset were moderately to highly reliable, with 46% exhibiting highly reliable values. Overall, the modeling framework suggests that molecular structure and chemical properties can together be effectively used to obtain first-cut estimates of the toxicokinetics of data-poor organic chemicals for screening and prioritization purposes.

Discovery of potent liver-selective stearoyl-CoA desaturase-1 (SCD1) inhibitors, thiazole-4-acetic acid derivatives, for the treatment of diabetes, hepatic steatosis, and obesity

SCD1 is a rate-limiting enzyme in the conversion of saturated fatty acids to monounsaturated fatty acids. SCD1 inhibitors have potential effects on obesity, diabetes, acne, and cancer, but the adverse effects associated with SCD1 inhibition in the skin and eyelids are impediments to clinical development. To avoid mechanism-based adverse effects, we explored the compounds that selectively inhibit SCD1 in the liver in an ex vivo assay. Starting from a systemically active lead compound, we focused on the physicochemical properties tPSA and cLogP to minimize exposure in the off-target tissues. This effort led to the discovery of thiazole-4-acetic acid analog 48 as a potent and liver-selective SCD1 inhibitor. Compound 48 exhibited significant effects in rodent models of diabetes, hepatic steatosis, and obesity, with sufficient safety margins in a rat toxicology study with repeated dosing.

Robust QSAR prediction models for volume of distribution at steady state in humans using relative distance measurements

The building of quantitative structure-activity relationship (QSAR) models for the in silico prediction of volume distribution for drugs at steady-state levels is vital for the selection of potential drugs at the synthesis stage. Using molecular descriptor matrixes, some regression models presenting low accuracy have been proposed, mainly due to the difficulty of compiling an appropriate dataset and the lack of information on dataset representation. In this paper, we use a benchmark dataset of very diverse drugs for the development of predictive models for volume distribution based on the use of relative distance matrixes as the input data to QSAR algorithms. Support vector machine, complex tree, bagged tree and Gaussian process regression algorithms were tested for fingerprint, similarity and relative distance matrixes used as input data, and the results of the built models were compared. Relative distance matrixes generated robust regression models in the training and external validation stages performed using cross-validation, obtaining values for correlation coefficient, bias, slope and root-mean-square error close to the ideal. Relative distance matrixes were also used for the design of classification models, obtaining excellent results with values of accuracy and area under receiver operating characteristic (AUC) close to 100%.

The In Vitro Pharmacological Profile of Drugs as a Proxy Indicator of Potential In Vivo Organ Toxicities

The potential of a drug to cause certain organ toxicities is somehow implicitly contained in its full pharmacological profile, provided the drug reaches and accumulates at the various organs where the different interacting proteins in its profile, both targets and off-targets, are expressed. Under this assumption, a computational approach was implemented to obtain a projected anatomical profile of a drug from its in vitro pharmacological profile linked to protein expression data across 47 organs. It was observed that the anatomical profiles obtained when using only the known primary targets of the drugs reflected roughly the intended organ targets. However, when both known and predicted secondary pharmacology was considered, the projected anatomical profiles of the drugs were able to clearly highlight potential organ off-targets. Accordingly, when applied to sets of drugs known to cause cardiotoxicity and hepatotoxicity, the approach is able to identify heart and liver, respectively, as the organs where the proteins in the pharmacological profile of the corresponding drugs are specifically expressed. When applied to a set of drugs linked to a risk of Torsades de Pointes, heart is again the organ clearly standing out from the rest and a potential protein profile hazard is proposed. The approach can be used as a proxy indicator of potential in vivo organ toxicities.

Recent progresses in the exploration of machine learning methods as in-silico ADME prediction tools

In-silico methods have been explored as potential tools for assessing ADME and ADME regulatory properties particularly in early drug discovery stages. Machine learning methods, with their ability in classifying diverse structures and complex mechanisms, are well suited for predicting ADME and ADME regulatory properties. Recent efforts have been directed at the broadening of application scopes and the improvement of predictive performance with particular focuses on the coverage of ADME properties, and exploration of more diversified training data, appropriate molecular features, and consensus modeling. Moreover, several online machine learning ADME prediction servers have emerged. Here we review these progresses and discuss the performances, application prospects and challenges of exploring machine learning methods as useful tools in predicting ADME and ADME regulatory properties.

Prediction of placental barrier permeability: a model based on partial least squares variable selection procedure

Assessing the human placental barrier permeability of drugs is very important to guarantee drug safety during pregnancy. Quantitative structure–activity relationship (QSAR) method was used as an effective assessing tool for the placental transfer study of drugs, while in vitro human placental perfusion is the most widely used method. In this study, the partial least squares (PLS) variable selection and modeling procedure was used to pick out optimal descriptors from a pool of 620 descriptors of 65 compounds and to simultaneously develop a QSAR model between the descriptors and the placental barrier permeability expressed by the clearance indices (CI). The model was subjected to internal validation by cross-validation and y-randomization and to external validation by predicting CI values of 19 compounds. It was shown that the model developed is robust and has a good predictive potential (r² = 0.9064, RMSE = 0.09, q² = 0.7323, r_p² = 0.7656, RMSP = 0.14). The mechanistic interpretation of the final model was given by the high variable importance in projection values of descriptors. Using PLS procedure, we can rapidly and effectively select optimal descriptors and thus construct a model with good stability and predictability. This analysis can provide an effective tool for the high-throughput screening of the placental barrier permeability of drugs.

The mechanisms of neurotoxicity and the selective vulnerability of nervous system sites

The spatial heterogeneity of the structure, function, and cellular composition of the nervous system confers extraordinary complexity and a multiplicity of mechanisms of chemical neurotoxicity. Because of its relatively high metabolic demands and functional dependence on postmitotic neurons, the nervous system is vulnerable to a variety of xenobiotics that affect essential homeostatic mechanisms that support function. Despite protection from the neuroglia and blood-brain barrier, the central nervous system is prone to attack from lipophilic toxicants and those that hijack endogenous transport, receptor, metabolic, and other biochemical systems. The inherent predilection of chemicals for highly conserved biochemical systems confers selective vulnerability of the nervous system to neurotoxicants. This chapter discusses selective vulnerability of the nervous system in the context of neuron-specific decrements (axonopathy, myelinopathy, disruption of neurotransmission), and the degree to which neuronal damage is facilitated or ameliorated by surrounding nonneural cells in both the central and peripheral nervous systems.