Identification of chemoresistance associated key genes-miRNAs-TFs in docetaxel resistant breast cancer by bioinformatics analysis.

The present study aimed to identify the differentially expressed genes (DEGs) and enriched pathways in docetaxel (DTX) resistant breast cancer cell lines by bioinformatics analysis. The microarray dataset GSE28784 was obtained from gene expression omnibus (GEO) database. The differentially expressed genes (DEGs), gene ontology (GO), and Kyoto Encyclopedia of gene and genome (KEGG) pathway analyses were performed with the help of GEO2R and DAVID tools. Furthermore, the protein-protein interaction (PPI) and hub-gene network of DEGs were constructed using STRING and Cytohubba tools. The prognostic values of hub genes were calculated with the help of the Kaplan-Meier plotter database. From the GEO2R analysis, 222 DEGs were identified of which 120 are upregulated and 102 are downregulated genes. In the PPIs network, five up-regulated genes including CCL2, SPARC, CYR61, F3, and MFGE8 were identified as hub genes. It was observed that low expression of six hub genes CXCL8, CYR61, F3, ICAM1, PLAT, and THBD were significantly correlated with poor overall survival of BC patients in survival analysis. miRNA analysis identified that hsa-mir-16-5p, hsa-mir-335-5p, hsa-mir-124-3p, hsa-mir-20a-5p, and hsa-mir-155-5p are the top 5 interactive miRNAs that are commonly interacting with more hub genes with degree score of greater than five. Additionally, drug-gene interaction analysis was performed to identify drugs which are could potentially elevate/lower the expression levels of hub genes. In summary, the gene-miRNAs-TFs network and subsequent correlation of candidate drugs with hub genes may improve individualized diagnosis and help select appropriate combination therapy for DTX-resistant BC in the future. Supplementary Information: The online version contains supplementary material available at 10.1007/s13205-024-03971-2.

Scaffold hopping for designing of potent and selective CYP1B1 inhibitors to overcome docetaxel resistance: synthesis and evaluation.

Cytochrome P450 1B1, a tumor-specific overexpressed enzyme, significantly impairs the pharmacokinetics of several commonly used anticancer drugs including docetaxel, paclitaxel and cisplatin, leading to the problem of resistance to these drugs. Currently, there is no CYP1B1 inhibition-based adjuvant therapy available to treat this resistance problem. Hence, in the current study, exhaustive in-silico studies including scaffold hopping followed by molecular docking, three-dimensional quantitative structure-activity relationships (3D-QSAR), molecular dynamics and free energy perturbation studies were carried out to identify potent and selective CYP1B1 inhibitors. Initially, scaffold hopping analysis was performed against a well-reported potent and selective CYP1B1 inhibitor (i.e. compound 3n). A total of 200 scaffolds were identified along with their shape and field similarity scores. The top three scaffolds were further selected on the basis of these scores and their synthesis feasibility to design some potent and selective CYP1B1 inhibitors using the aforementioned in-silico techniques. Designed molecules were further synthesized to evaluate their CYP1B1 inhibitory activity and docetaxel resistance reversal potential against CYP1B1 overexpressed drug resistance MCF-7 cell line. In-vitro results indicated that compounds 2a, 2c and 2d manifested IC50 values for CYP1B1 ranging from 0.075, 0.092 to 0.088 µM with at least 10-fold selectivity. At low micromolar concentrations, compounds 1e, 1f, 2a and 2d exhibited promising cytotoxic effects in the docetaxel-resistant CYP1B1 overexpressed MCF-7 cell line. In particular, compound 2a is most effective in reversing the resistance with IC50 of 29.0 ± 3.6 µM. All of these discoveries could pave the way for the development of adjuvant therapy capable of overcoming CYP1B1-mediated resistance.Communicated by Ramaswamy H. Sarma.

Scaffold hopping based designing of selective ALDH1A1 inhibitors to overcome cyclophosphamide resistance: synthesis and biological evaluation.

Aldehyde dehydrogenase 1A1 (ALDH1A1) is an isoenzyme that catalyzes the conversion of aldehydes to acids. However, the overexpression of ALDH1A1 in a variety of malignancies is the major cause of resistance to an anti-cancer drug, cyclophosphamide (CP). CP is a prodrug that is initially converted into 4-hydroxycyclophosphamide and its tautomer aldophosphamide, in the liver. These compounds permeate into the cell and are converted as active metabolites, i.e., phosphoramide mustard (PM), through spontaneous beta-elimination. On the other hand, the conversion of CP to PM is diverted at the level of aldophosphamide by converting it into inactive carboxyphosphamide using ALDH1A1, which ultimately leads to high drug inactivation and CP resistance. Hence, in combination with our earlier work on the target of resistance, i.e., ALDH1A1, we hereby report selective ALDH1A1 inhibitors. Herein, we selected a lead molecule from our previous virtual screening and implemented scaffold hopping analysis to identify a novel scaffold that can act as an ALDH1A1 inhibitor. This results in the identification of various novel scaffolds. Among these, on the basis of synthetic feasibility, the benzimidazole scaffold was selected for the design of novel ALDH1A1 inhibitors, followed by machine learning-assisted structure-based virtual screening. Finally, the five best compounds were selected and synthesized. All synthesized compounds were evaluated using in vitro enzymatic assay against ALDH1A1, ALDH2, and ALDH3A1. The results disclosed that three molecules A1, A2, and A3 showed significant selective ALDH1A1 inhibitory potential with an IC50 value of 0.32 µM, 0.55 µM, and 1.63 µM, respectively, and none of the compounds exhibits potency towards the other two ALDH isoforms i.e. ALDH2 and ALDH3A1. Besides, the potent compounds (A1, A2, and A3) have been tested for in vitro cell line assay in combination with mafosfamide (analogue of CP) on two cell lines i.e. A549 and MIA-PaCa-2. All three compounds show significant potency to reverse mafosfamide resistance by inhibiting ALDH1A1 against these cell lines.

Raloxifene and bazedoxifene as selective ALDH1A1 inhibitors to ameliorate cyclophosphamide resistance: A drug repurposing approach.

Cyclophosphamide (CP) is one of the most widely used anticancer drugs for various malignancies. However, its long-term use leads to ALDH1A1-mediated inactivation and subsequent resistance which necessitates the development of potential ALDH1A1 inhibitors. Currently, ALDH1A1 inhibitors from different chemical classes have been reported, but these failed to reach the market due to safety and efficacy problems. Developing a new treatment from the ground requires a huge amount of time, effort, and money, therefore it is worthwhile to improve CP efficacy by proposing better adjuvants as ALDH1A1 inhibitors. Herein, the database constituting the FDA-approved drugs with well-established safety and toxicity profiles was screened through already reported machine learning models by our research group. This model is validated for discriminating the ALDH1A1 inhibitors and non-inhibitors. Virtual screening protocol (VS) from this model identified four FDA-approved drugs, raloxifene, bazedoxifene, avanafil, and betrixaban as selective ALDH1A1 inhibitors. The molecular docking, dynamics, and water swap analysis also suggested these drugs to be promising ALDH1A1 inhibitors which were further validated for their CP resistance reversal potential by in-vitro analysis. The in-vitro enzymatic assay results indicated that raloxifene and bazedoxifene selectively inhibited the ALDH1A1 enzyme with IC50 values of 2.35 and 4.41 µM respectively, whereas IC50 values of both the drugs against ALDH2 and ALDH3A1 was >100 µM. Additional in-vitro studies with well-reported ALDH1A1 overexpressing A549 and MIA paCa-2 cell lines suggested that mafosfamide sensitivity was further ameliorated by the combination of both raloxifene and bazedoxifene. Collectively, in-silico and in-vitro studies indicate raloxifene and bazedoxifene act as promising adjuvants with CP that may improve the quality of treatment for cancer patients with minimal toxicities.

Identification of natural peptides from "PlantPepDB" database as anti-SARS-CoV-2 agents: A protein-protein docking approach.

Background: A global pandemic owing to COVID-19 infection has created havoc in the entire world. The etiological agent responsible for this viral outbreak is classified as severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). Still, there's no specific drug or preventive medication to treat SARS-CoV-2. This study was designed to demonstrate the efficacy of some anti-viral peptides obtained from a plant database i.e., PlantPepDB as potential ACE-2-Spike (S) protein complex neutralizers using a structure-based drug designing approach. Method: A total of 83 anti-viral plant peptides were screened from a peptide database i.e. PlantPepDB based on their reported anti-viral activities against various viral strains. In order to screen peptides that may potentially interfere with ACE-2 and S complex formation, molecular docking studies were conducted using the flare module of Cresset software and subsequently, analysed the crucial interactions between the peptides and S complexes and ACE-2/S complex. Herein, the interactions and docking scores obtained for ACE-2/S complex were considered as references. The S-peptides complexes which displayed superior interactions and docking scores than reference complex i.e., ACE2-S were considered as final hits. The Molecular dynamics studies were conducted for a period of 30 ns for each of the final hit/S complex to understand the interaction stability and binding mechanism of designed peptides. Results: The molecular docking results revealed that five peptides including Cycloviolacin Y3, Cycloviolacin Y1, White cloud bean defensin, Putative defensin 3.1, and Defensin D1 showed superior docking scores (i.e. -1372.5 kJ/mol to -1232.6 kJ/mol) when docked at the ACE2 binding site of S-protein than score obtained for the complex of ACE-2 and S protein i.e. -1183.4 kJ/mol. Moreover, these top five peptides manifested key interactions required to prevent the binding of S protein with ACE2. The molecular dynamics simulation study revealed that two of these five peptides i.e. Cycloviolacin Y3 and Cycloviolacin Y1 displayed minimal RMSD fluctuations. Conclusions: The current structure-based drug-designing approach shows the possible role of anti-viral plant peptides as potential molecules to be explored at the initial stage of viral pathogenesis.

In silico guided designing of optimized benzochalcones derivatives as potent CYP1B1 inhibitors: An integrated in vitro and ONIOM study.

Cytochrome P4501B1 (CYP1B1) is reported to be overexpressed in various malignancies including ovarian, lung, lymph, and breast cancers. The overexpression of this enzyme is accountable for the biotransformation-based inactivation of some anti-cancer drugs i.e. Docetaxel, Paclitaxel, and Cisplatin. To circumvent solutions to this issue, the current study reports some optimized derivatives of benzochalcone as selective CYP1B1 inhibitors. The optimized derivatives were screened using some structure-based drug-designing approaches including molecular docking and molecular dynamics. The implemented approaches revealed that all the designed molecules demonstrated not only essential interactions with key amino acid residues but also maintained stability within the active site of CYP1B1. Furthermore, to validate the in-silico results and develop a SAR, the designed molecules were subsequently synthesized and tested for their ability to selectively inhibit CYP1B1 over CYP1A1 using well established EROD assay. This assay results suggested that compounds 1(c), 1(d), and 1(e) are eightfold more selective CYP1B1 inhibitors over CYP1A1 with IC50 values ranging from 0.06 to 0.09 µM respectively. Among these, compound 1(d) manifested potent inhibitory activity i.e. IC50 of 0.06 µM with 24 folds selectivity over 1A1. To have a better insight into the binding pattern of 1(d) within CYP1B1 and precisely compute binding affinity for 1(d)-CYP1B1 complex, one of the advanced QM/MM approaches i.e. ONIOM has been implemented. Where 1(d)-CYP1B1 complex conferred comparable binding affinity in terms of ΔG (kcal/mol) with that of ANF-CYP1B1 complex. This research could provide a suitable starting point for the development of more potent multi-functional compounds with CYP1B1 inhibitory activity.

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.

Dihydropyrimidine Dehydrogenase-Mediated Resistance to 5-Fluorouracil: Mechanistic Investigation and Solution.

5-Fluorouracil (5-FU) is one of the most widely used chemotherapeutics for the treatment of cancers associated with the aerodigestive tract, breast, and colorectal system. The efficacy of 5-FU is majorly affected by dihydropyrimidine dehydrogenase (DPD) as it degrades more than 80% of administered 5-FU into an inactive metabolite, dihydrofluorouracil. Herein we discuss the molecular mechanism of this inactivation by analyzing the interaction pattern and electrostatic complementarity of the DPD-5-FU complex. The basis of DPD overexpression in cancer cell lines due to significantly distinct levels of the miRNAs (miR-134, miR-27b, and miR-27a) compared to normal cells has also been outlined. Additionally, some kinases including sphingosine kinase 2 (SphK2) have been reported to correlate with DPD expression. Currently, to address this problem various strategies are reported in the literature, including 5-FU analogues (bypass the DPD-mediated inactivation), DPD downregulators (regulate the DPD expression levels in tumors), inhibitors (as promising adjuvants), and formulation development loaded with 5-FU (liposomes, nanoparticles, nanogels, etc.), which are briefly discussed in this Review.

Role of Genetic Polymorphisms in Drug-Metabolizing Enzyme-Mediated Toxicity and Pharmacokinetic Resistance to Anti-Cancer Agents: A Review on the Pharmacogenomics Aspect.

The inter-individual differences in cancer susceptibility are somehow correlated with the genetic differences that are caused by the polymorphisms. These genetic variations in drug-metabolizing enzymes/drug-inactivating enzymes may negatively or positively affect the pharmacokinetic profile of chemotherapeutic agents that eventually lead to pharmacokinetic resistance and toxicity against anti-cancer drugs. For instance, the CYP1B1*3 allele is associated with CYP1B1 overexpression and consequent resistance to a variety of taxanes and platins, while 496T>G is associated with lower levels of dihydropyrimidine dehydrogenase, which results in severe toxicities related to 5-fluorouracil. In this context, a pharmacogenomics approach can be applied to ascertain the role of the genetic make-up in a person's response to any drug. This approach collectively utilizes pharmacology and genomics to develop effective and safe medications that are devoid of resistance problems. In addition, recently reported genomics studies revealed the impact of many single nucleotide polymorphisms in tumors. These studies emphasized the importance of single nucleotide polymorphisms in drug-metabolizing enzymes on the effect of anti-tumor drugs. In this review, we discuss the pharmacogenomics aspect of polymorphisms in detail to provide an insight into the genetic manipulations in drug-metabolizing enzymes that are responsible for pharmacokinetic resistance or toxicity against well-known anti-cancer drugs. Special emphasis is placed on different deleterious single nucleotide polymorphisms and their effect on pharmacokinetic resistance. The information provided in this report may be beneficial to researchers, especially those who are working in the field of biotechnology and human genetics, in rationally manipulating the genetic information of patients with cancer who are undergoing chemotherapy to avoid the problem of pharmacokinetic resistance/toxicity associated with drug-metabolizing enzymes.

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.

3D-QSAR and scaffold hopping based designing of benzo[d]ox-azol-2(3H)-one and 2-oxazolo[4,5-b]pyridin-2(3H)-one derivatives as selective aldehyde dehydrogenase 1A1 inhibitors: Synthesis and biological evaluation.

Aldehyde dehydrogenase 1 (ALDH1A1), an oxidoreductase class of enzymes, is overexpressed in various types of cancer cell lines and is the major cause of resistance to the Food and Drug Administration (FDA)-approved drug, cyclophosphamide (CP). In cancer conditions, CP undergoes a sequence of biotransformations to form an active metabolite, aldophosphamide, which further biotransforms to its putative cytotoxic metabolite, phosphoramide mustard. However, in resistant cancer conditions, aldophosphamide is converted into its inactive metabolite, carboxyphosphamide, via oxidation with ALDH1A1. Herein, to address the issue of ALDH1A1 mediated CP resistance, we report a series of benzo[d]oxazol-2(3H)-one and 2-oxazolo[4,5-b]pyridin-2(3H)-one derivatives as selective ALDH1A1 inhibitors. These inhibitors were designed using a validated 3D-quantitative structure activity relationship (3D-QSAR) model coupled with scaffold hopping. The 3D-QSAR model was developed using reported indole-2,3-diones based ALDH1A1 inhibitors, which provided field points in terms of electrostatic, van der Waals and hydrophobic potentials required for selectively inhibiting ALDH1A1. The most selective indole-2,3-diones-based compound, that is, cmp 3, was further considered for scaffold hopping. Two top-ranked bioisosteres, that is, benzo[d]oxazol-2(3H)-one and 2-oxazolo[4,5-b]pyridin-2(3H)-one, were selected for designing new inhibitors by considering the field pattern of 3D-QSAR. All designed molecules were mapped perfectly on the 3D-QSAR model and found to be predictive with good inhibitory potency (pIC50 range: 7.5-6.8). Molecular docking was carried out for each designed molecule to identify key interactions that are required for ALDH1A1 inhibition and to authenticate the 3D-QSAR result. The top five inhibitor-ALDH1A1 complexes were also submitted for molecular dynamics simulations to access their stability. In vitro enzyme assays of 21 compounds suggested that these compounds are selective toward ALDH1A1 over the other two isoforms, that is, ALDH2 and ALDH3A1. All the compounds were found to be at least three and two times more selective toward ALDH1A1 over ALDH2 and ALDH3A1, respectively. All the compounds showed an IC50 value in the range of 0.02-0.80 µM, which indicates the potential for these to be developed as adjuvant therapy for CP resistance.

Molecular Docking, Dynamics, and WaterSwap Analysis to Identify Anti-aggregating Agents of Insulin and IFN-ß.

There are several challenges in the development, and formulation of biologics, particularly concerning their physical stabilities. The self-assembly of peptides like human insulin and interferon beta (IFN-ß) has potential to form aggregates in pharmaceutical formulation. Therefore, it is a significant problem in the manufacturing, storage, and delivery of insulin and IFN-ß formulations. Amino acids as aggregation suppressing additives have been used to stabilize proteins during manufacturing and storage. Several changes to the B chain's C-terminus have been proposed in an attempt to improve insulin formulation. The core segments of the A and B chains (SLYQLENY and LVEALYLV) have recently been identified as sheet-forming areas, and their microcrystalline structures have been exploited to construct a high-resolution insulin amyloid fibril model. Here, we have chosen twenty-one amino acids to develop as additives in rendering the insulin and IFN-ß aggregations. Thereafter, integrated molecular docking studies of single layer monomers of full-length insulin and IFN-ß have been performed to identify structural elements (amino acids) that can act as disaggregating agents. The stability of the best-docked amino acid complexes was judged using molecular dynamics studies. Finally, phenylalanine was identified as a disaggregation agent for insulin, and lysine, tyrosine, phenylalanine, and tryptophan were identified as disaggregation agents for IFN-ß from the molecular dynamics study. These findings may open a novel proposal to explore further in vitro studies to increase the stability of the insulin and IFN-ß formulation.

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.

Identification of potential genes associated with ALDH1A1 overexpression and cyclophosphamide resistance in chronic myelogenous leukemia using network analysis.

Cyclophosphamide (CP), an important alkylating agent which is used in the treatment therapy for chronic myeloid leukemia (CML). However, acquired drug resistance owing to the inactivation of its active metabolite aldophosphamide via tumoral-overexpressing aldehyde dehydrogenase (ALDH1A1) is one of the major issues with the CP therapy. However, the underlying mechanism of ALDH1A1 overexpression in cancer cells remains poorly defined. Therefore, the current study focused on analyzing the ALDH1A1-overexpressing microarray data for CP resistance and CP-sensitive CML cell lines. In this study, the microarray dataset was obtained from Gene Expression Omnibus GEO. The GEO2R tool was used to identify Differentially Expressing Genes (DEGs). Further, protein-protein interaction (PPI) network of DEGs were constructed using STRING database. Finally, Hub gene-miRNA-TFs interaction were constructed using miRNet tool. A total of 749 DEGs including 387 upregulated and 225 downregulated genes were identified from this pool of microarray data. The construction of DEGs network resulted in identification of three genes including ZEB2, EZH2, and MUC1 were found to be majorly responsible for ALDH1A1 overexpression. miRNA analysis identified that, hsa-mir-16-5p and hsa-mir-26a-5p as hub miRNA which are commonly interacting with maximum target genes. Additionally, drug-gene interaction analysis was performed to identify drugs which are responsible for ALDH1A1 expression. The entire study may provide a deeper understanding about ALDH1A1 regulatory genes responsible for its overexpression in CP resistance cancer. This understanding may be further explore for developing possible co-therapy to avoid the ALDH1A1-mediated CP resistance.

Identification of kinase inhibitors that rule out the CYP27B1-mediated activation of vitamin D: an integrated machine learning and structure-based drug designing approach.

CYP27B1, a cytochrome P450-containing hydroxylase enzyme, converts vitamin D precursor calcidiol (25-hydroxycholecalciferol) to its active form calcitriol (1α,25(OH)2D3). Tyrosine kinase inhibitor such as imatinib is reported to interfere with the activation of vitamin D3 by inhibiting CYP27B1 enzyme. Consequently, there is a decrease in the serum levels of active vitamin D that in turn may increase the relapse risk among the cancer patients treated with imatinib. Within this framework, the current study focuses on identifying other possible kinase inhibitors that may affect the calcitriol level in the body by inhibiting CYP27B1. To achieve this, we explored multiple machine learning approaches including support vector machine (SVM), random forest (RF), and artificial neural network (ANN) to identify possible CYP27B1 inhibitors from a pool of kinase inhibitors database. The most reliable classification model was obtained from the SVM approach with Matthews correlation coefficient of 0.82 for the external test set. This model was further employed for the virtual screening of kinase inhibitors from the binding database (DB), which tend to interfere with the CYP27B1-mediated activation of vitamin D. This screening yielded around 4646 kinase inhibitors that were further subjected to structure-based analyses using the homology model of CYP27B1, as the 3D structure of CYP27B1 complexed with heme was not available. Overall, five kinase inhibitors including two well-known drugs, i.e., AT7867 (Compound-2) and amitriptyline N-oxide (Compound-3), were found to interact with CYP27B1 in such a way that may preclude the conversion of vitamin D to its active form and hence testify the impairment of vitamin D activation pathway.

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.

Molecular modeling approaches to address drug-metabolizing enzymes (DMEs) mediated chemoresistance: a review.

Resistance against clinically approved anticancer drugs is the main roadblock in cancer treatment. Drug metabolizing enzymes (DMEs) that are capable of metabolizing a variety of xenobiotic get overexpressed in malignant cells, therefore, catalyzing drug inactivation. As evident from the literature reports, the levels of DMEs increase in cancer cells that ultimately lead to drug inactivation followed by drug resistance. To puzzle out this issue, several strategies inclusive of analog designing, prodrug designing, and inhibitor designing have been forged. On that front, the implementation of computational tools can be considered a fascinating approach to address the problem of chemoresistance. Various research groups have adopted different molecular modeling tools for the investigation of DMEs mediated toxicity problems. However, the utilization of these in-silico tools in maneuvering the DME mediated chemoresistance is least considered and yet to be explored. These tools can be employed in the designing of such chemotherapeutic agents that are devoid of the resistance problem. The current review canvasses various molecular modeling approaches that can be implemented to address this issue. Special focus was laid on the development of specific inhibitors of DMEs. Additionally, the strategies to bypass the DMEs mediated drug metabolism were also contemplated in this report that includes analogs and pro-drugs designing. Different strategies discussed in the review will be beneficial in designing novel chemotherapeutic agents that depreciate the resistance problem.