Multiple Perspectives in Anti-cancer Drug Discovery: From old Targets and Natural Products to Innovative Computational Approaches

The Tobacco β-Cembrenediol: A Prostate Cancer Recurrence Suppressor Lead and Prospective Scaffold via Modulation of Indoleamine 2,3-Dioxygenase and Tryptophan Dioxygenase

Prostate cancer (PC) is the second leading cause of death in men in the US. PC has a high recurrence rate, and limited therapeutic options are available to prevent disease recurrence. The tryptophan-degrading enzymes 2,3-indoleamine dioxygenase (IDO1) and tryptophan dioxygenase (TDO2) are upregulated in invasive PC. (1S,2E,4R,6R,7E,11E)-2,7,11-cembratriene-4,6-diol (β-CBT) and its C-4 epimer α-CBT are the precursors to key flavor ingredients in tobacco leaves. Nearly 40-60% of β- and α-CBT are purposely degraded during commercial tobacco fermentation. Earlier, β-CBT inhibited invasion, reversed calcitonin-stimulated transepithelial resistance decrease, and induced tighter intercellular barriers in PC-3M cells. This study demonstrates the in vitro β-CBT anti-migratory (wound-healing assay) and anti-clonogenicity (colony-formation assay) activities against five diverse human PC cell lines, including the androgen-independent PC-3, PC-3M, and DU-145, the castration-recurrent CWR-R1ca, and the androgen-dependent CWR-22rv1. Meanwhile, β-CBT potently suppressed in vivo locoregional and distant recurrences after the primary tumor surgical excision of PC-3M-Luc cell tumor engrafted in male nude mice. β-CBT treatments suppressed organ and bone metastasis and lacked any major toxicity over the 60-day study course. β-CBT treatments significantly suppressed IDO1, TDO2, and their final metabolite kynurenine levels in PC-3M cells. β-CBT treatments significantly suppressed the tumor recurrence marker PSA and kynurenine levels in treated animals' plasma. β-CBT emerges as a promising PC recurrence suppressive lead.

An aggregation-induced emission fluorescence probe for evaluating the effect of CYP450 changes under tumor chemotherapy

Cancer is a complex disease with very high incidence and mortality rates every year. However, cancer drug resistance greatly mitigates the cure rates of tumors, and cytochrome P450 (CYP450) plays an important role in the development of cisplatin resistance. We developed the aggregation-induced emission luminogen (AIEgen) TPE-CYP to monitor the changes in CYP450. The TPE-CYP fluorescent probe was successfully used to assess CYP450 levels in tumor cells and tumor tissue sections. This study presented that CYP450 level in HepG2/DDP cells (cisplatin-resistant cells) was higher than that in HepG2 cells, and the inhibition of CYP450 by 1-ABT effectively improved the tumor resistance. Thus, CYP450 plays a key role in the development of tumor resistance. The synergistic effect of 1-ABT and the chemotherapeutic agent cisplatin was superior to that of cisplatin alone in tumor-bearing mice. The TPE-CYP probe will provide an idea for the clinical implementation of individualized tumor treatment strategies, through the accurate monitoring of CYP450.

Comparative Gene Signature of (-)-Oleocanthal Formulation Treatments in Heterogeneous Triple Negative Breast Tumor Models: Oncological Therapeutic Target Insights

Triple negative breast cancer (TNBC) heterogeneity and limited therapeutic options confer its phenotypic aggressiveness. The discovery of anti-TNBC natural products with valid molecular target(s) and defined pharmacodynamic profile would facilitate their therapeutic nutraceutical use by TNBC patients. The extra-virgin olive oil (EVOO) is a key Mediterranean diet ingredient. S-(−)-Oleocanthal (OC) leads the bioactive anti-tumor EVOO phenolic ingredients. A previous study reported the solid dispersion formulated OC with (+)-xylitol (OC-X) suppressed the in vivo progression and recurrence of the TNBC MDA-MB-231 cells. This study investigates the ability of OC-X formulation to suppress the in vivo heterogeneous BC initiation and progression utilizing advanced preclinical transgenic MMTV-PyVT and TNBC PDX mouse models. Furthermore, the clustering of the gene expression profiles in MMTV-PyVT and PDX mouse tumors treated with OC-X acquired by a Clariom S microarray analysis identified the distinctly affected genes. Several affected novel signature genes identified in response to OC-X treatments and proved overlapped in both mouse and human tumor models, shedding some lights toward understanding the OC anticancer molecular mechanism and assisting in predicting prospective clinical outcomes. This study provides molecular and preclinical evidences of OC-X potential as a nutraceutical suppressing heterogeneous TNBC model and offers preliminary gene-level therapeutic mechanistic insights.

AKT Inhibitors: The Road Ahead to Computational Modeling-Guided Discovery

AKT, is a serine/threonine protein kinase comprising three isoforms-namely: AKT1, AKT2 and AKT3, whose inhibitors have been recognized as promising therapeutic targets for various human disorders, especially cancer. In this work, we report a systematic evaluation of multi-target Quantitative Structure-Activity Relationship (mt-QSAR) models to probe AKT' inhibitory activity, based on different feature selection algorithms and machine learning tools. The best predictive linear and non-linear mt-QSAR models were found by the genetic algorithm-based linear discriminant analysis (GA-LDA) and gradient boosting (Xgboost) techniques, respectively, using a dataset containing 5523 inhibitors of the AKT isoforms assayed under various experimental conditions. The linear model highlighted the key structural attributes responsible for higher inhibitory activity whereas the non-linear model displayed an overall accuracy higher than 90%. Both these predictive models, generated through internal and external validation methods, were then used for screening the Asinex kinase inhibitor library to identify the most potential virtual hits as pan-AKT inhibitors. The virtual hits identified were then filtered by stepwise analyses based on reverse pharmacophore-mapping based prediction. Finally, results of molecular dynamics simulations were used to estimate the theoretical binding affinity of the selected virtual hits towards the three isoforms of enzyme AKT. Our computational findings thus provide important guidelines to facilitate the discovery of novel AKT inhibitors.

MCDCalc: Markov Chain Molecular Descriptors Calculator for Medicinal Chemistry

AIMS

Cheminformatics models are able to predict different outputs (activity, property, chemical reactivity) in single molecules or complex molecular systems (catalyzed organic synthesis, metabolic reactions, nanoparticles, etc.).

BACKGROUND

Cheminformatics models are able to predict different outputs (activity, property, chemical reactivity) in single molecules or complex molecular systems (catalyzed organic synthesis, metabolic reactions, nanoparticles, etc.).

OBJECTIVE

Cheminformatics prediction of complex catalytic enantioselective reactions is a major goal in organic synthesis research and chemical industry. Markov Chain Molecular Descriptors (MCDs) have been largely used to solve Cheminformatics problems. There are different types of Markov chain descriptors such as Markov-Shannon entropies (Shk), Markov Means (Mk), Markov Moments (πk), etc. However, there are other possible MCDs that have not been used before. In addition, the calculation of MCDs is done very often using specific software not always available for general users and there is not an R library public available for the calculation of MCDs. This fact, limits the availability of MCMDbased Cheminformatics procedures.

METHODS

We studied the enantiomeric excess ee(%)[Rcat] for 324 α-amidoalkylation reactions. These reactions have a complex mechanism depending on various factors. The model includes MCDs of the substrate, solvent, chiral catalyst, product along with values of time of reaction, temperature, load of catalyst, etc. We tested several Machine Learning regression algorithms. The Random Forest regression model has R2 > 0.90 in training and test. Secondly, the biological activity of 5644 compounds against colorectal cancer was studied.

RESULTS

We developed very interesting model able to predict with Specificity and Sensitivity 70-82% the cases of preclinical assays in both training and validation series.

CONCLUSION

The work shows the potential of the new tool for computational studies in organic and medicinal chemistry.