A quest for the stereo-electronic requirements for selective agonism for the neurotrophin receptors TrkA and TrkB in 17-spirocyclic-dehydroepiandrosterone derivatives.

Introduction: The neurotrophin system plays a pivotal role in the development, morphology, and survival of the nervous system, and its dysregulation has been manifested in numerous neurodegenerative and neuroinflammatory diseases. Neurotrophins NGF and BDNF are major growth factors that prevent neuronal death and synaptic loss through binding with high affinity to their specific tropomyosin-related kinase receptors namely, TrkA and TrkB, respectively. The poor pharmacokinetic properties prohibit the use of neurotrophins as therapeutic agents. Our group has previously synthesized BNN27, a prototype small molecule based on dehydroepiandrosterone, mimicking NGF through the activation of the TrkA receptor. Methods: To obtain a better understanding of the stereo-electronic requirements for selective activation of TrkA and TrkB receptors, 27 new dehydroepiandrosterone derivatives bearing a C17-spiro-dihydropyran or cyclobutyl moiety were synthesized. The new compounds were evaluated for their ability (a) to selectively activate the TrkA receptor and its downstream signaling kinases Akt and Erk1/2 in PC12 cells, protecting these cells from serum deprivation-induced cell death, and (b) to induce phosphorylation of TrkB and to promote cell survival under serum deprivation conditions in NIH3T3 cells stable transfected with the TrkB receptor and primary cortical astrocytes. In addition the metabolic stability and CYP-mediated reaction was assessed. Results: Among the novel derivatives, six were able to selectively protect PC12 cells through interaction with the TrkA receptor and five more to selectively protect TrkB-expressing cells via interaction with the TrkB receptor. In particular, compound ENT-A025 strongly induces TrkA and Erk1/2 phosphorylation, comparable to NGF, and can protect PC12 cells against serum deprivation-induced cell death. Furthermore, ENT-A065, ENT-A066, ENT-A068, ENT-A069, and ENT-A070 showed promising pro-survival effects in the PC12 cell line. Concerning TrkB agonists, ENT-A009 and ENT-A055 were able to induce phosphorylation of TrkB and reduce cell death levels in NIH3T3-TrkB cells. In addition, ENT-A076, ENT-A087, and ENT-A088 possessed antiapoptotic activity in NIH-3T3-TrkB cells exclusively mediated through the TrkB receptor. The metabolic stability and CYP-mediated reaction phenotyping of the potent analogs did not reveal any major liabilities. Discussion: We have identified small molecule selective agonists of TrkA and TrkB receptors as promising lead neurotrophin mimetics for the development of potential therapeutics against neurodegenerative conditions.

A Robust Machine Learning Framework Built Upon Molecular Representations Predicts CYP450 Inhibition: Toward Precision in Drug Repurposing.

Human cytochrome P450 (CYP450) enzymes play a crucial role in drug metabolism and pharmacokinetics. CYP450 inhibition can lead to toxicity, in particular when drugs are co-administered with other drugs and xenobiotics or in the case of polypharmacy. Predicting CYP450 inhibition is also important for rational drug discovery and development, and precision in drug repurposing. In this overarching context, digital transformation of drug discovery and development, for example, using machine and deep learning approaches, offers prospects for prediction of CYP450 inhibition through computational models. We report here the development of a majority-voting machine learning framework to classify inhibitors and noninhibitors for seven major human liver CYP450 isoforms (CYP1A2, CYP2A6, CYP2B6, CYP2C9, CYP2C19, CYP2D6, and CYP3A4). For the machine learning models reported herein, we employed interaction fingerprints that were derived from molecular docking simulations, thus adding an additional layer of information for protein-ligand interactions. The proposed machine learning framework is based on the structure of the binding site of isoforms to produce predictions beyond previously reported approaches. Also, we carried out a comparative analysis so as to identify which representation of test compounds (molecular descriptors, molecular fingerprints, or protein-ligand interaction fingerprints) affects the predictive performance of the models. This work underlines the ways in which the structure of the enzyme catalytic site influences machine learning predictions and the need for robust frameworks toward better-informed predictions.

Bioenergetic Profiling in Glioblastoma Multiforme Patients with Different Clinical Outcomes.

The accumulation of cell biomass is associated with dramatically increased bioenergetic and biosynthetic demand. Metabolic reprogramming, once thought as an epiphenomenon, currently relates to disease progression, also in response to extracellular fate-decisive signals. Glioblastoma multiforme patients often suffer misdiagnosis, short survival time, low quality of life, and poor disease management options. Today, tumor genetic testing and histological analysis guide diagnosis and treatment. We and others appreciate that metabolites complement translational biomarkers and molecular signatures in disease profiling and phenotyping. Herein, we coupled a mixed-methods content analysis to a mass spectrometry-based untargeted metabolomic analysis on plasma samples from glioblastoma multiforme patients to delineate the role of metabolic remodeling in biological plasticity and, hence, disease severity. Following data processing and analysis, we established a bioenergetic profile coordinated by the mitochondrial function and redox state, lipids, and energy substrates. Our findings show that epigenetic modulators are key players in glioblastoma multiforme cell metabolism, in particular when microRNAs are considered. We propose that biological plasticity in glioblastoma multiforme is a mechanism of adaptation and resistance to treatment which is eloquently revealed by bioenergetics.

Development of a multigram synthesis of the bradykinin receptor 2 agonist FR-190997 and analogs thereof.

Using Fujisawa's B2R agonist FR-190997, we recently demonstrated for the first time that agonism at the bradykinin receptor type 2 (B2R) produces substantial antiproliferative effects. FR-190997 elicited an EC50 of 80 nM in the triple-negative breast cancer cell line MDA-MB-231, a much superior performance to that exhibited by most approved breast cancer drugs. Consequently, we initiated a program aiming primarily at synthesizing adequate quantities of FR-190997 to support further in vitro and in vivo studies toward its repurposing for various cancers and, in parallel, enable the generation of novel FR-190997 analogs for an SAR study. Prerequisite for this endeavor was to address the synthetic challenges associated with the FR-190997 scaffold, which the Fujisawa chemists had constructed in 20 steps, 13 of which required chromatographic purification. We succeeded in developing a 17-step synthesis amenable to late-stage diversification that eliminated all chromatography and enabled access to multigram quantities of FR-190997 and novel derivatives thereof, supporting further anticancer research based on B2R agonists.

Pharmacometabolomics-Based Translational Biomarkers: How to Navigate the Data Ocean.

Metabolome is the end point of the genome-environment interplay, and enables an important holistic overview of individual adaptability and host responses to environmental, ecological, as well as endogenous changes such as disease. Pharmacometabolomics is the application of metabolome knowledge to decipher the mechanisms of interindividual and intraindividual variations in drug efficacy and safety. Pharmacometabolomics also contributes to prediction of drug treatment outcomes on the basis of baseline (predose) and postdose metabotypes through mathematical modeling. Thus, pharmacometabolomics is a strong asset for a diverse community of stakeholders interested in theory and practice of evidence-based and precision/personalized medicine: academic researchers, public health scholars, health professionals, pharmaceutical, diagnostics, and biotechnology industries, among others. In this expert review, we discuss pharmacometabolomics in four contexts: (1) an interdisciplinary omics tool and field to map the mechanisms and scale of interindividual variability in drug effects, (2) discovery and development of translational biomarkers, (3) advance digital biomarkers, and (4) empower drug repurposing, a field that is increasingly proving useful in the current era of Covid-19. As the applications of pharmacometabolomics are growing rapidly in the current postgenome era, next-generation proteomics and metabolomics follow the example of next-generation sequencing analyses. Pharmacometabolomics can also empower data reliability and reproducibility through multiomics integration strategies, which use each data layer to correct, connect with, and inform each other. Finally, we underscore here that contextual data remain crucial for precision medicine and drug development that stand the test of time and clinical relevance.

A 3-miRNA Signature Enables Risk Stratification in Glioblastoma Multiforme Patients with Different Clinical Outcomes.

Malignant gliomas constitute a complex disease phenotype that demands optimum decision-making as they are highly heterogeneous. Such inter-individual variability also renders optimum patient stratification extremely difficult. microRNA (hsa-miR-20a, hsa-miR-21, hsa-miR-21) expression levels were determined by RT-qPCR, upon FFPE tissue sample collection of glioblastoma multiforme patients (n = 37). In silico validation was then performed through discriminant analysis. Immunohistochemistry images from biopsy material were utilized by a hybrid deep learning system to further cross validate the distinctive capability of patient risk groups. Our standard-of-care treated patient cohort demonstrates no age- or sex- dependence. The expression values of the 3-miRNA signature between the low- (OS > 12 months) and high-risk (OS < 12 months) groups yield a p-value of <0.0001, enabling risk stratification. Risk stratification is validated by a. our random forest model that efficiently classifies (AUC = 97%) patients into two risk groups (low- vs. high-risk) by learning their 3-miRNA expression values, and b. our deep learning scheme, which recognizes those patterns that differentiate the images in question. Molecular-clinical correlations were drawn to classify low- (OS > 12 months) vs. high-risk (OS < 12 months) glioblastoma multiforme patients. Our 3-microRNA signature (hsa-miR-20a, hsa-miR-21, hsa-miR-10a) may further empower glioblastoma multiforme prognostic evaluation in clinical practice and enrich drug repurposing pipelines.