Design and synthesis of selective CYP1B1 inhibitor via dearomatization of α-naphthoflavone

Design, synthesis and structure-activity relationship of 1,8-naphthalimide derivatives as highly potent hCYP1B1 inhibitors

Human cytochrome P450 1B1 enzyme (hCYP1B1), a member of hCYP1 subfamily, plays a crucial role in multiple diseases by participating in many metabolic pathways. Although a suite of potent hCYP1B1 inhibitors have been previously reported, most of them also act as aryl hydrocarbon receptor (AhR) agonists that can up-regulate the expression of hCYP1B1 and then counteract their inhibitory potential in living systems. This study aimed to develop novel efficacious hCYP1B1 inhibitors that worked well in living cells but without AhR agonist effects. For these purposes, a series of 1,8-naphthalimide derivatives were designed and synthesized, and their structure-activity relationships (SAR) as hCYP1B1 inhibitors were analyzed. Following three rounds SAR studies, several potent hCYP1B1 inhibitors were discovered, among which compound 3n was selected for further investigations owing to its extremely potent anti-hCYP1B1 activity (IC50 = 0.040 nM) and its blocking AhR transcription activity in living cells. Inhibition kinetic analyses showed that 3n potently inhibited hCYP1B1 via a mix inhibition manner, showing a Ki value of 21.71 pM. Docking simulations suggested that introducing a pyrimidine moiety to the hit compound (1d) facilitated 3n to form two strong interactions with hCYP1B1/heme, viz., the C-Br⋯π halogen bond and the N-Fe coordination bond. Further investigations demonstrated that 3n (5 μM) could significantly reverse the paclitaxel (PTX) resistance in H460/PTX cells, evidenced by the dramatically reduced IC50 values, from 632.6 nM (PTX alone) to 100.8 nM (PTX plus 3n). Collectively, this study devised a highly potent hCYP1B1 inhibitor (3n) without AhR agonist effect, which offered a promising drug candidate for overcoming hCYP1B1-associated drug resistance.

3D-QSAR assisted identification of selective CYP1B1 inhibitors: an effective bioisosteric replacement/molecular docking/electrostatic complementarity analysis

Cytochrome P450-1B1 is a majorly overexpressed drug-metabolizing enzyme in tumors and is responsible for inactivation and subsequent resistance to a variety of anti-cancer drugs, i.e., docetaxel, tamoxifen, and cisplatin. In the present study, a 3D quantitative structure-activity relationship (3D-QSAR) model has been constructed for the identification, design, and optimization of novel CYP1B1 inhibitors. The model has been built using a set of 148 selective CYP1B1 inhibitors. The developed model was evaluated based on certain statistical parameters including q2 and r2 which showed the acceptable predictive and descriptive capability of the generated model. The developed 3D-QSAR model assisted in understanding the key molecular fields which were firmly related to the selective CYP1B1 inhibition. A theoretic approach for the generation of new lead compounds with optimized CYP1B1 receptor affinity has been performed utilizing bioisosteric replacement analysis. These generated molecules were subjected to a developed 3D-QSAR model to predict the inhibitory activity potentials. Furthermore, these compounds were scrutinized through the activity atlas model, molecular docking, electrostatic complementarity, molecular dynamics, and waterswap analysis. The final hits might act as selective CYP1B1 inhibitors which could address the issue of resistance. This 3D-QSAR includes several chemically diverse selective CYP1B1 receptor ligands and well accounts for the individual ligand's inhibition affinities. These features of the developed 3D-QSAR model will ensure future prospective applications of the model to speed up the identification of new potent and selective CYP1B1 receptor ligands.

Melatonin Activation by Human Cytochrome P450 Enzymes: A Comparison between Different Isozymes

Cytochrome P450 enzymes in the human body play a pivotal role in both the biosynthesis and the degradation of the hormone melatonin. Melatonin plays a key role in circadian rhythms in the body, but its concentration is also linked to mood fluctuations as well as emotional well-being. In the present study, we present a computational analysis of the binding and activation of melatonin by various P450 isozymes that are known to yield different products and product distributions. In particular, the P450 isozymes 1A1, 1A2, and 1B1 generally react with melatonin to provide dominant aromatic hydroxylation at the C6-position, whereas the P450 2C19 isozyme mostly provides O-demethylation products. To gain insight into the origin of these product distributions of the P450 isozymes, we performed a comprehensive computational study of P450 2C19 isozymes and compared our work with previous studies on alternative isozymes. The work covers molecular mechanics, molecular dynamics and quantum mechanics approaches. Our work highlights major differences in the size and shape of the substrate binding pocket amongst the different P450 isozymes. Consequently, substrate binding and positioning in the active site varies substantially within the P450 isozymes. Thus, in P450 2C19, the substrate is oriented with its methoxy group pointing towards the heme, and therefore reacts favorably through hydrogen atom abstraction, leading to the production of O-demethylation products. On the other hand, the substrate-binding pockets in P450 1A1, 1A2, and 1B1 are tighter, direct the methoxy group away from the heme, and consequently activate an alternative site and lead to aromatic hydroxylation instead.

Hydration and Structural Adaptations of the Human CYP1A1, CYP1A2, and CYP1B1 Active Sites by Molecular Dynamics Simulations

Cytochromes CYP1A1, CYP1A2, and CYP1B1, the members of the cytochrome P450 family 1, catalyze the metabolism of endogenous compounds, drugs, and non-drug xenobiotics which include substances involved in the process of carcinogenesis, cancer chemoprevention, and therapy. In the present study, the interactions of three selected polymethoxy-trans-stilbenes, analogs of a bioactive polyphenol trans-resveratrol (3,5,4'-trihydroxy-trans-stilbene) with the binding sites of CYP1 isozymes were investigated with molecular dynamics (MD) simulations. The most pronounced structural changes in the CYP1 binding sites were observed in two substrate recognition sites (SRS): SRS2 (helix F) and SRS3 (helix G). MD simulations show that the number and position of water molecules occurring in CYP1 APO and in the structures complexed with ligands are diverse. The presence of water in binding sites results in the formation of water-protein, water-ligand, and bridging ligand-water-protein hydrogen bonds. Analysis of the solvent and substrate channels opening during the MD simulation showed significant differences between cytochromes in relation to the solvent channel and the substrate channels 2c, 2ac, and 2f. The results of this investigation lead to a deeper understanding of the molecular processes that occur in the CYP1 binding sites and may be useful for further molecular studies of CYP1 functions.

Artificial Intelligence in Drug Metabolism and Excretion Prediction: Recent Advances, Challenges, and Future Perspectives

Drug metabolism and excretion play crucial roles in determining the efficacy and safety of drug candidates, and predicting these processes is an essential part of drug discovery and development. In recent years, artificial intelligence (AI) has emerged as a powerful tool for predicting drug metabolism and excretion, offering the potential to speed up drug development and improve clinical success rates. This review highlights recent advances in AI-based drug metabolism and excretion prediction, including deep learning and machine learning algorithms. We provide a list of public data sources and free prediction tools for the research community. We also discuss the challenges associated with the development of AI models for drug metabolism and excretion prediction and explore future perspectives in the field. We hope this will be a helpful resource for anyone who is researching in silico drug metabolism, excretion, and pharmacokinetic properties.

A new class of CYP1B1 inhibitors derived from bentranil

Cytochrome P450 1B1 (CYP1B1) is highly expressed in a variety of tumors and implicated to drug resistance. More and more researches have suggested that CYP1B1 is a new target for cancer prevention and therapy. Various CYP1B1 inhibitors with a rigid polycyclic skeleton have been developed, such as flavonoids, trans-stilbenes, and quinazolines. To obtain a new class of CYP1B1 inhibitors, we designed and synthesized a series of bentranil analogues, moreover, IC₅₀ determinations were performed for CYP1B1 inhibition of five of these compounds and found that 6o and 6q were the best inhibitors, with IC₅₀ values in the nM range. The selectivity index (SI) of CYP1B1 over CYP1A1 and CYP1A2 was 30-fold higher than that of α-naphthoflavone (ANF). The molecular docking results showed that compound 6q fitted better into the CYP1B1 binding site than other compounds, which was consistent with our experimental results. On the basis of 6o and 6q, it is expected to develop CYP1B1 inhibitors with stronger affinity, higher selectivity and better solubility.

Design of Cytochrome P450 1B1 Inhibitors via a Scaffold-Hopping Approach

Cytochrome P450 1B1 (CYP1B1) is a potential drug target in cancer research that is overexpressed in several solid tumors but is present only at low levels in healthy tissues. Its expression is associated with resistance to common chemotherapeutics, while inhibitors restore efficacy to these drugs in model systems. The majority of CYP1B1 inhibitors are derived from a limited number of scaffolds, and few have achieved outstanding selectivity against other human CYPs, which could impede clinical development. This study explores a new chemical space for CYP1B1 inhibitors using a scaffold-hopping approach and establishes 2,4-diarylthiazoles as a promising framework for further development. From a small library, compound 15 emerged as the lead, with picomolar CYP1B1 inhibition, and over 19,000-fold selectivity against its relative, CYP1A1. To investigate the activity of 15, molecular dynamics, optical spectroscopy, point mutations, and traditional structure-activity relationships were employed and revealed key interactions important for the development of CYP1B1 inhibitors.

Computational Investigation of Ligand Binding of Flavonoids in Cytochrome P450 Receptors

AIM

The cytochrome P450 enzymes play a significant role in regulating cellular and physiological processes by activating endogenous compounds. They also play an essential role in the detoxification process of xenobiotics. Flavonoids belong to a class of polyphenols found in food, such as vegetables, red wine, beer, and fruits, which modulate biological functions in the body.

METHODS

The inhibition of CYP1A1 and CYP1B1 using nutritional sources has been reported as a strategy for cancer prevention. This study investigated the interactions of selected flavonoids binding to the cytochrome P450 enzymes (CYP1A1 and CYP1B1) and their ADMET properties in silico. From docking studies, our findings showed flavonoids, isorhamnetin and pedalitin, to have the strongest binding energies in the crystal structures 6DWM and 6IQ5.

RESULTS

The amino acid residues Asp 313 and Phe 224 in 6DWM interacted with all the ligands investigated, and Ala 330 in 6IQ5 interacted with all the ligands examined. The ligands did not violate any drug-likeness parameters.

CONCLUSION

These data suggest roles for isorhamnetin and pedalitin as potential precursors for natural product- derived therapies.

Machine Learning Enabled Structure-Based Drug Repurposing Approach to Identify Potential CYP1B1 Inhibitors

Drug-metabolizing enzyme (DME)-mediated pharmacokinetic resistance of some clinically approved anticancer agents is one of the main reasons for cancer treatment failure. In particular, some commonly used anticancer medicines, including docetaxel, tamoxifen, imatinib, cisplatin, and paclitaxel, are inactivated by CYP1B1. Currently, no approved drugs are available to treat this CYP1B1-mediated inactivation, making the pharmaceutical industries strive to discover new anticancer agents. Because of the extreme complexity and high risk in drug discovery and development, it is worthwhile to come up with a drug repurposing strategy that may solve the resistance problem of existing chemotherapeutics. Therefore, in the current study, a drug repurposing strategy was implemented to find the possible CYP1B1 inhibitors using machine learning (ML) and structure-based virtual screening (SB-VS) approaches. Initially, three different ML models were developed such as support vector machines (SVMs), random forest (RF), and artificial neural network (ANN); subsequently, the best-selected ML model was employed for virtual screening of the selleckchem database to identify potential CYP1B1 inhibitors. The inhibition potency of the obtained hits was judged by analyzing the crucial active site amino acid interactions against CYP1B1. After a thorough assessment of docking scores, binding affinities, as well as binding modes, four compounds were selected and further subjected to in vitro analysis. From the in vitro analysis, it was observed that chlorprothixene, nadifloxacin, and ticagrelor showed promising inhibitory activity toward CYP1B1 in the IC₅₀ range of 0.07-3.00 μM. These new chemical scaffolds can be explored as adjuvant therapies to address CYP1B1-mediated drug-resistance problems.

Multiple machine learning models combined with virtual screening and molecular docking to identify selective human ALDH1A1 inhibitors

Aldehyde dehydrogenases (ALDHs) are the enzymes of oxidoreductase family that are responsible for the aldehyde metabolism. The unbalanced expression of these enzymes may be associated with a variety of disease conditions including cancers. ALDH1A1 is one of the isoform of ALDHs majorly overexpressed in a variety of tumors and responsible for the anti-cancer drug resistance. This makes ALDH1A1 as a specific target to develop small molecule ALDH1A1 inhibitors for resistant cancer condition. Number of ALDH1A1 inhibitors have been developed and reported in the literature, but because of non-selectivity and inappropriate pharmacokinetic properties till now none of these have reached in the market for clinical use. Therefore, multiple machine learning models of different isoforms of ALDHs are integrated with in-silico techniques including virtual screening, docking, ADMET profiling, and MD simulation to identify selective ALDH1A1 inhibitors. Total ten selective ALDH1A1 inhibitors with diverse scaffolds and appropriate ADMET were identified that can be further developed as adjuvant therapy in cyclophosphamide and cisplatin resistance cancer.

New Perspectives of CYP1B1 Inhibitors in the Light of Molecular Studies

Human cytochrome P450 1B1 (CYP1B1) is an extrahepatic heme-containing monooxygenase. CYP1B1 contributes to the oxidative metabolism of xenobiotics, drugs, and endogenous substrates like melatonin, fatty acids, steroid hormones, and retinoids, which are involved in diverse critical cellular functions. CYP1B1 plays an important role in the pathogenesis of cardiovascular diseases, hormone-related cancers and is responsible for anti-cancer drug resistance. Inhibition of CYP1B1 activity is considered as an approach in cancer chemoprevention and cancer chemotherapy. CYP1B1 can activate anti-cancer prodrugs in tumor cells which display overexpression of CYP1B1 in comparison to normal cells. CYP1B1 involvement in carcinogenesis and cancer progression encourages investigation of CYP1B1 interactions with its ligands: substrates and inhibitors. Computational methods, with a simulation of molecular dynamics (MD), allow the observation of molecular interactions at the binding site of CYP1B1, which are essential in relation to the enzyme’s functions.

Multiple machine learning, molecular docking, and ADMET screening approach for identification of selective inhibitors of CYP1B1

Cytochrome P4501B1 is a ubiquitous family protein that is majorly overexpressed in tumors and is responsible for biotransformation-based inactivation of anti-cancer drugs. This inactivation marks the cause of resistance to chemotherapeutics. In the present study, integrated in-silico approaches were utilized to identify selective CYP1B1 inhibitors. To achieve this objective, we initially developed different machine learning models corresponding to two isoforms of the CYP1 family i.e. CYP1A1 and CYP1B1. Subsequently, small molecule databases including ChemBridge, Maybridge, and natural compound library were screened from the selected models of CYP1B1 and CYP1A1. The obtained CYP1B1 inhibitors were further subjected to molecular docking and ADMET analysis. The selectivity of the obtained hits for CYP1B1 over the other isoforms was also judged with molecular docking analysis. Finally, two hits were found to be the most stable which retained key interactions within the active site of CYP1B1 after the molecular dynamics simulations. Novel compound with CYP-D9 and CYP-14 IDs were found to be the most selective CYP1B1 inhibitors which may address the issue of resistance. Moreover, these compounds can be considered as safe agents for further cell-based and animal model studies.Communicated by Ramaswamy H. Sarma.

Inhibition of human CYP1 enzymes by a classical inhibitor α-naphthoflavone and a novel inhibitor N-(3, 5-dichlorophenyl)cyclopropanecarboxamide: An in vitro and in silico study

Enzymes in the cytochrome P450 family 1 (CYP1) catalyze metabolic activation of procarcinogens and deactivation of certain anticancer drugs. Inhibition of these enzymes is a potential approach for cancer chemoprevention and treatment of CYP1-mediated drug resistance. We characterized inhibition of human CYP1A1, CYP1A2, and CYP1B1 enzymes by the novel inhibitor N-(3,5-dichlorophenyl)cyclopropanecarboxamide (DCPCC) and α-naphthoflavone (ANF). Depending on substrate, IC₅₀ values of DCPCC for CYP1A1 or CYP1B1 were 10-95 times higher than for CYP1A2. IC₅₀ of DCPCC for CYP1A2 was 100-fold lower than for enzymes in CYP2 and CYP3 families. DCPCC IC₅₀ values were 10-680 times higher than the ones of ANF. DCPCC was a mixed-type inhibitor of CYP1A2. ANF was a competitive tight-binding inhibitor of CYP1A1, CYP1A2, and CYP1B1. CYP1A1 oxidized DCPCC more rapidly than CYP1A2 or CYP1B1 to the same metabolite. Molecular dynamics simulations and binding free energy calculations explained the differences of binding of DCPCC and ANF to the active sites of all three CYP1 enzymes. We conclude that DCPCC is a more selective inhibitor for CYP1A2 than ANF. DCPCC is a candidate structure to modulate CYP1A2-mediated metabolism of procarcinogens and anticancer drugs.