Molecular Modeling Studies on Benzimidazole Carboxamide Derivatives as PARP-1 Inhibitors Using 3D-QSAR and Docking

Preclinical evaluation of a brain penetrant PARP PET imaging probe in rat glioblastoma and nonhuman primates

PURPOSE

Currently, there are multiple active clinical trials involving poly(ADP-ribose) polymerase (PARP) inhibitors in the treatment of glioblastoma. The noninvasive quantification of baseline PARP expression using positron emission tomography (PET) may provide prognostic information and lead to more precise treatment. Due to the lack of brain-penetrant PARP imaging agents, the reliable and accurate in vivo quantification of PARP in the brain remains elusive. Herein, we report the synthesis of a brain-penetrant PARP PET tracer, (R)-2-(2-methyl-1-(methyl-11C)pyrrolidin-2-yl)-1H-benzo[d]imidazole-4-carboxamide ([11C]PyBic), and its preclinical evaluations in a syngeneic RG2 rat glioblastoma model and healthy nonhuman primates.

METHODS

We synthesized [11C]PyBic using veliparib as the labeling precursor, performed dynamic PET scans on RG2 tumor-bearing rats and calculated the distribution volume ratio (DVR) using simplified reference region method 2 (SRTM2) with the contralateral nontumor brain region as the reference region. We performed biodistribution studies, western blot, and immunostaining studies to validate the in vivo PET quantification results. We characterized the brain kinetics and binding specificity of [11C]PyBic in nonhuman primates on FOCUS220 scanner and calculated the volume of distribution (VT), nondisplaceable volume of distribution (VND), and nondisplaceable binding potential (BPND) in selected brain regions.

RESULTS

[11C]PyBic was synthesized efficiently in one step, with greater than 97% radiochemical and chemical purity and molar activity of 148 ± 85 MBq/nmol (n = 6). [11C]PyBic demonstrated PARP-specific binding in RG2 tumors, with 74% of tracer binding in tumors blocked by preinjected veliparib (i.v., 5 mg/kg). The in vivo PET imaging results were corroborated by ex vivo biodistribution, PARP1 immunohistochemistry and immunoblotting data. Furthermore, brain penetration of [11C]PyBic was confirmed by quantitative monkey brain PET, which showed high specific uptake (BPND > 3) and low nonspecific uptake (VND < 3 mL/cm3) in the monkey brain.

CONCLUSION

[11C]PyBic is the first brain-penetrant PARP PET tracer validated in a rat glioblastoma model and healthy nonhuman primates. The brain kinetics of [11C]PyBic are suitable for noninvasive quantification of available PARP binding in the brain, which posits [11C]PyBic to have broad applications in oncology and neuroimaging.

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.

A multi-task FP-GNN framework enables accurate prediction of selective PARP inhibitors

PARP (poly ADP-ribose polymerase) family is a crucial DNA repair enzyme that responds to DNA damage, regulates apoptosis, and maintains genome stability; therefore, PARP inhibitors represent a promising therapeutic strategy for the treatment of various human diseases including COVID-19. In this study, a multi-task FP-GNN (Fingerprint and Graph Neural Networks) deep learning framework was proposed to predict the inhibitory activity of molecules against four PARP isoforms (PARP-1, PARP-2, PARP-5A, and PARP-5B). Compared with baseline predictive models based on four conventional machine learning methods such as RF, SVM, XGBoost, and LR as well as six deep learning algorithms such as DNN, Attentive FP, MPNN, GAT, GCN, and D-MPNN, the evaluation results indicate that the multi-task FP-GNN method achieves the best performance with the highest average BA, F1, and AUC values of 0.753 ± 0.033, 0.910 ± 0.045, and 0.888 ± 0.016 for the test set. In addition, Y-scrambling testing successfully verified that the model was not results of chance correlation. More importantly, the interpretability of the multi-task FP-GNN model enabled the identification of key structural fragments associated with the inhibition of each PARP isoform. To facilitate the use of the multi-task FP-GNN model in the field, an online webserver called PARPi-Predict and its local version software were created to predict whether compounds bear potential inhibitory activity against PARPs, thereby contributing to design and discover better selective PARP inhibitors.

Recent Progress of the research on the benzimidazole PARP-1 inhibitors

Poly (ADP-ribose) polymerase-1 (PARP-1) is a multifunctional protein that plays an important role in DNA repair and genome integrity. PARP-1 inhibitors can be used as effective drugs not only to treat BRCA-1/2 deficient cancers because of the effect of synthetically lethal, but also to treat non-BRCA1/2 deficient tumours because of the effect of PARP capture. Therefore, the PARP inhibitors have become a focus of compelling research. Among these inhibitors, substituted benzimidazole derivatives were mainly concerned lead compounds. However, the commercial available benzimidazole PARP-1 inhibitors have some shortcomings such as serious toxicity in combination with chemotherapy drugs, in vivo cardiovascular side effects such as anemia. Therefore it's crucial for scientists to explore more structure-activity relationships of the benzimidazole PARP-1 inhibitors and access safer and more effective PARP inhibitors. As the binding region of PARP-1 and the substrates is usually characterized as NI site and AD site, the modification of benzimidazoles mainly occurs on the benzimidazole skeleton (NI site), and the side chain of benzimidazole on 2-C position (AD site). Herein, the recent progresses of the researches of benzamides PARP inhibitors were introduced. We noticed that even though many efforts were taken to the modification of NI sites, there were still lacks of optimistic and impressive results. However, the structure-activity relationships of the modification of AD sites have not thoroughly discovered yet. We hope that enlightened by the previous researches, more researches of AD site should be occurred and more effective benzimidazole PARP-1 inhibitors could be designed, synthesized, and applied to clinics.

Discovery of the PARP (poly ADP-ribose polymerase) inhibitor 2-(1-(4,4-difluorocyclohexyl)piperidin-4-yl)-1H-benzo[d]imidazole-4-carboxamide for the treatment of cancer

In this work, two series of cyclic amine-containing benzimidazole carboxamide derivatives were designed and synthesized as potent anticancer agents. PARP1/2 inhibitory activity assays indicated that most of the compounds showed significant activity. The in vitro antiproliferative activity of these compounds was investigated against four human cancer cell lines (MDA-MB-436, MDA-MB-231, MCF-7 and CAPAN-1), and several compounds exhibited strong cytotoxicity to tumor cells. Among them, 2-(1-(4,4-difluorocyclohexyl)piperidin-4-yl)-1H-benzo[d]imidazole-4-carboxamide (17d) was found to be effective PARP1/2 inhibitors (IC50 = 4.30 and 1.58 nM, respectively). In addition, 17d possessed obvious selective antineoplastic activity and noteworthy microsomal metabolic stability. What's more, further studies revealed that 17d was endowed with an excellent ADME profile. These combined results indicated that 17d could be a promising candidate for the treatment of cancer.

QSAR Study of PARP Inhibitors by GA-MLR, GA-SVM and GA-ANN Approaches

Background:

The poly(ADP-ribose) polymerases (PARP) is a nuclear enzyme superfamily present in eukaryotes.

Methods:

In the present report, some efficient linear and non-linear methods including multiple linear regression (MLR), support vector machine (SVM) and artificial neural networks (ANN) were successfully used to develop and establish quantitative structure-activity relationship (QSAR) models capable of predicting pEC50 values of tetrahydropyridopyridazinone derivatives as effective PARP inhibitors. Principal component analysis (PCA) was used to a rational division of the whole data set and selection of the training and test sets. A genetic algorithm (GA) variable selection method was employed to select the optimal subset of descriptors that have the most significant contributions to the overall inhibitory activity from the large pool of calculated descriptors.

Results:

The accuracy and predictability of the proposed models were further confirmed using crossvalidation, validation through an external test set and Y-randomization (chance correlations) approaches. Moreover, an exhaustive statistical comparison was performed on the outputs of the proposed models. The results revealed that non-linear modeling approaches, including SVM and ANN could provide much more prediction capabilities.

Conclusion:

Among the constructed models and in terms of root mean square error of predictions (RMSEP), cross-validation coefficients (Q2 LOO and Q2 LGO), as well as R2 and F-statistical value for the training set, the predictive power of the GA-SVM approach was better. However, compared with MLR and SVM, the statistical parameters for the test set were more proper using the GA-ANN model.

The cardioprotective effect of dexrazoxane (Cardioxane) is consistent with sequestration of poly(ADP-ribose) by self-assembly and not depletion of topoisomerase 2B

Following systematic scrutiny of the evidence in support of the hypothesis that the cardioprotective mechanism of action of dexrazoxane is mediated by a 'depletion' or 'downregulation' of Top2β protein levels in heart tissue, the author concludes that this hypothesis is untenable. In seeking to understand how dexrazoxane protects the heart, the outcomes of a customised association rule learning algorithm incorporating the use of antecedent surrogate variables (CEME, 2017 McCormack Pharma) reveal a previously unknown relationship between dexrazoxane and poly(ADP-ribose) (PAR) polymer. The author shows how this previously unknown relationship explains both acute and long-term cardioprotection in patients receiving anthracyclines. In addition, as a direct inhibitor of PAR dexrazoxane has access to the epigenome and this offers a new insight into protection by dexrazoxane against doxorubicin-induced late-onset damage [McCormack K, manuscript in preparation]. Notably, through this review article, the author illustrates the practical application of probing natural language text using an association rule learning algorithm for the discovery of new and interesting associations that, otherwise, would remain lost. Historically, the use of CEME enabled the first report of the capacity of a small molecule to catalyse the hybrid self-assembly of a nucleic acid biopolymer via canonical and non-canonical, non-covalent interactions analogous to Watson Crick and Hoogsteen base pairing, respectively.

Development of a human dihydroorotate dehydrogenase (hDHODH) pharma-similarity index approach with scaffold-hopping strategy for the design of novel potential inhibitors

Human dihydroorotate dehydrogenase (hDHODH) is a class-2 dihydroorotate dehydrogenase. Because it is extensively used by proliferating cells, its inhibition in autoimmune and inflammatory diseases, cancers, and multiple sclerosis is of substantial clinical importance. In this study, we had two aims. The first was to develop an hDHODH pharma-similarity index approach (PhSIA) using integrated molecular dynamics calculations, pharmacophore hypothesis, and comparative molecular similarity index analysis (CoMSIA) contour information techniques. The approach, for the discovery and design of novel inhibitors, was based on 25 diverse known hDHODH inhibitors. Three statistical methods were used to verify the performance of hDHODH PhSIA. Fischer’s cross-validation test provided a 98% confidence level and the goodness of hit (GH) test score was 0.61. The q², r², and predictive r² values were 0.55, 0.97, and 0.92, respectively, for a partial least squares validation method. In our approach, each diverse inhibitor structure could easily be aligned with contour information, and common substructures were unnecessary. For our second aim, we used the proposed approach to design 13 novel hDHODH inhibitors using a scaffold-hopping strategy. Chemical features of the approach were divided into two groups, and the Vitas-M Laboratory fragment was used to create de novo inhibitors. This approach provides a useful tool for the discovery and design of potential inhibitors of hDHODH, and does not require docking analysis; thus, our method can assist medicinal chemists in their efforts to identify novel inhibitors.

Synthetic approaches and pharmacological activity of 1,3,4-oxadiazoles: a review of the literature from 2000-2012

This review provides readers with an overview of the main synthetic methodologies for 1,3,4-oxadiazole derivatives, and of their broad spectrum of pharmacological activities as reported over the past twelve years.