Computational exploration of microsomal cytochrome P450 3A1 enzyme modulation by phytochemicals of Cichorium intybus L.: Insights into drug metabolism

Drug metabolism plays a crucial role in drug fate, including therapeutic inactivation or activation, as well as the formation of toxic compounds. This underscores the importance of understanding drug metabolism in drug discovery and development. Considering the substantial costs associated with traditional drug development methods, computational approaches have emerged as valuable tools for predicting the metabolic fate of drug candidates. With this in mind, the present study aimed to investigate the potential mechanisms underlying the modulation of microsomal cytochrome P450 3A1 (CYP3A1) enzyme activity by various phytochemicals found in Cichorium intybus L., commonly known as chicory. To achieve this goal, several in silico methods, including molecular docking and molecular dynamics (MD) simulation, were employed to explore computationally the microsomal CYP3A1 enzyme. Schrodinger software was utilized for the molecular docking study, which involved the interaction analysis between CYP3A1 and 28 phytoconstituents of Cichorium intybus. Virtual screening of 28 compounds from chicory led to the identification of the top five ranked compounds. These compounds were evaluated for drug-likeness properties, pharmacokinetic profiles, and predicted binding affinities to CYP3A1. Caffeoylshikimic acid and cichoric acid emerged as promising candidates due to their favorable characteristics, including good oral bioavailability and high binding affinities to CYP3A1. Molecular dynamics simulations were conducted to assess the stability of caffeoylshikimic acid within the CYP3A1 binding pocket. The results demonstrated that caffeoylshikimic acid maintained stable interactions with the enzyme throughout the simulation, suggesting its potential as an effective modulator of CYP3A1 activity. The findings of this study have the potential to provide valuable insights into the complex molecular mechanisms by which Cichorium intybus L. acts on hepatocytes and modulates CYP3A1 enzyme expression or activity. By elucidating the impact of these phytochemicals on drug metabolism, this research contributes to our understanding of how chicory may interact with drugs and influence their efficacy and safety profiles.

Exploring new targets and chemical space with affinity selection-mass spectrometry

Affinity selection-mass spectrometry (AS-MS) is a high-throughput screening (HTS) technique for drug discovery that enables rapid screening of large collections of compounds to identify ligands for a specific biomolecular target. AS-MS is a binding assay that is insensitive to the functional effects a ligand might have, which is important because it lets us identify novel ligands irrespective of their binding site. This approach is gaining popularity, notably due to its role in the emergence of useful agents for targeted protein degradation. This Perspective highlights the use of AS-MS techniques to explore broad chemical space and identify small-molecule ligands for biological targets that have proven challenging to address with other screening paradigms. We present chemical structures of reported AS-MS hits to illustrate the potential of this screening approach to deliver high-quality hits for further optimization. AS-MS has, thus, evolved from being an infrequent alternative to traditional HTS or DNA-encoded library strategies to now firmly establishing itself as a HTS approach for drug discovery.

ADMET-score - a comprehensive scoring function for evaluation of chemical drug-likeness

Chemical absorption, distribution, metabolism, excretion, and toxicity (ADMET), play key roles in drug discovery and development. A high-quality drug candidate should not only have sufficient efficacy against the therapeutic target, but also show appropriate ADMET properties at a therapeutic dose. A lot of in silico models are hence developed for prediction of chemical ADMET properties. However, it is still not easy to evaluate the drug-likeness of compounds in terms of so many ADMET properties. In this study, we proposed a scoring function named the ADMET-score to evaluate drug-likeness of a compound. The scoring function was defined on the basis of 18 ADMET properties predicted via our web server admetSAR. The weight of each property in the ADMET-score was determined by three parameters: the accuracy rate of the model, the importance of the endpoint in the process of pharmacokinetics, and the usefulness index. The FDA-approved drugs from DrugBank, the small molecules from ChEMBL and the old drugs withdrawn from the market due to safety concerns were used to evaluate the performance of the ADMET-score. The indices of the arithmetic mean and p-value showed that the ADMET-score among the three data sets differed significantly. Furthermore, we learned that there was no obvious linear correlation between the ADMET-score and QED (quantitative estimate of drug-likeness). These results suggested that the ADMET-score would be a comprehensive index to evaluate chemical drug-likeness, and might be helpful for users to select appropriate drug candidates for further development.

Selective Inhibition of the Mitochondrial Permeability Transition Pore Protects against Neurodegeneration in Experimental Multiple Sclerosis

The mitochondrial permeability transition pore is a recognized drug target for neurodegenerative conditions such as multiple sclerosis and for ischemia-reperfusion injury in the brain and heart. The peptidylprolyl isomerase, cyclophilin D (CypD, PPIF), is a positive regulator of the pore, and genetic down-regulation or knock-out improves outcomes in disease models. Current inhibitors of peptidylprolyl isomerases show no selectivity between the tightly conserved cyclophilin paralogs and exhibit significant off-target effects, immunosuppression, and toxicity. We therefore designed and synthesized a new mitochondrially targeted CypD inhibitor, JW47, using a quinolinium cation tethered to cyclosporine. X-ray analysis was used to validate the design concept, and biological evaluation revealed selective cellular inhibition of CypD and the permeability transition pore with reduced cellular toxicity compared with cyclosporine. In an experimental autoimmune encephalomyelitis disease model of neurodegeneration in multiple sclerosis, JW47 demonstrated significant protection of axons and improved motor assessments with minimal immunosuppression. These findings suggest that selective CypD inhibition may represent a viable therapeutic strategy for MS and identify quinolinium as a mitochondrial targeting group for in vivo use.

Analysis of Physicochemical Properties for Drugs of Natural Origin

The impact of time, therapy area, and route of administration on 13 physicochemical properties calculated for 664 drugs developed from a natural prototype was investigated. The mean values for the majority of properties sampled over five periods from pre-1900 to 2013 were found to change in a statistically significant manner. In contrast, lipophilicity and aromatic ring count remained relatively constant, suggesting that these parameters are the most important for successful prosecution of a natural product drug discovery program if the route of administration is not focused exclusively on oral availability. An examination by therapy area revealed that anti-infective agents had the most differences in physicochemical property profiles compared with other areas, particularly with respect to lipophilicity. However, when this group was removed, the variation between the mean values for lipophilicity and aromatic ring count across the remaining therapy areas was again found not to change in a meaningful manner, further highlighting the importance of these two parameters. The vast majority of drugs with a natural progenitor were formulated for either oral and/or injectable administration. Injectables were, on average, larger and more polar than drugs developed for oral, topical, and inhalation routes.

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.

Guiding principles for natural product drug discovery

Natural products (NPs) have historically been a fertile source of new drugs for the pharmaceutical industry. However, this once-popular approach has waned considerably over the past two decades as the high-throughput screening of megalibraries comprised mainly of molecules with non-natural (synthetic) motifs has unfolded. Contemporary high-throughput screening libraries contain molecules compliant with physicochemical profiles considered essential for downstream development. Until recently, there was no strategy that aligned NP screening with the same physicochemical profiles. An approach based on Log P has addressed these concerns and, together with advances in isolation, afforded NP leads in timelines compatible with pure compound screening. Concomitant progress related to access of biological resources has provided long-awaited legal certainty to further facilitate NP drug discovery.

Established and emerging trends in computational drug discovery in the structural genomics era

Bioinformatics and chemoinformatics approaches contribute to hit discovery, hit-to-lead optimization, safety profiling, and target identification and enhance our overall understanding of the health and disease states. A vast repertoire of computational methods has been reported and increasingly combined in order to address more and more challenging targets or complex molecular mechanisms in the context of large-scale integration of structure and bioactivity data produced by private and public drug research. This review explores some key computational methods directly linked to drug discovery and chemical biology with a special emphasis on compound collection preparation, virtual screening, protein docking, and systems pharmacology. A list of generally freely available software packages and online resources is provided, and examples of successful applications are briefly commented upon.