Advanced bioinformatics rapidly identifies existing therapeutics for patients with coronavirus disease-2019 (COVID-19).

BACKGROUND: The recent global pandemic has placed a high priority on identifying drugs to prevent or lessen clinical infection of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), caused by Coronavirus disease-2019 (COVID-19). METHODS: We applied two computational approaches to identify potential therapeutics. First, we sought to identify existing FDA approved drugs that could block coronaviruses from entering cells by binding to ACE2 or TMPRSS2 using a high-throughput AI-based binding affinity prediction platform. Second, we sought to identify FDA approved drugs that could attenuate the gene expression patterns induced by coronaviruses, using our Disease Cancelling Technology (DCT) platform. RESULTS: Top results for ACE2 binding iincluded several ACE inhibitors, a beta-lactam antibiotic, two antiviral agents (Fosamprenavir and Emricasan) and glutathione. The platform also assessed specificity for ACE2 over ACE1, important for avoiding counterregulatory effects. Further studies are needed to weigh the benefit of blocking virus entry against potential counterregulatory effects and possible protective effects of ACE2. However, the data herein suggest readily available drugs that warrant experimental evaluation to assess potential benefit. DCT was run on an animal model of SARS-CoV, and ranked compounds by their ability to induce gene expression signals that counteract disease-associated signals. Top hits included Vitamin E, ruxolitinib, and glutamine. Glutathione and its precursor glutamine were highly ranked by two independent methods, suggesting both warrant further investigation for potential benefit against SARS-CoV-2. CONCLUSIONS: While these findings are not yet ready for clinical translation, this report highlights the potential use of two bioinformatics technologies to rapidly discover existing therapeutic agents that warrant further investigation for established and emerging disease processes.

Pridopidine activates neuroprotective pathways impaired in Huntington Disease.

Pridopidine has demonstrated improvement in Huntington Disease (HD) motor symptoms as measured by secondary endpoints in clinical trials. Originally described as a dopamine stabilizer, this mechanism is insufficient to explain the clinical and preclinical effects of pridopidine. This study therefore explored pridopidine's potential mechanisms of action. The effect of pridopidine versus sham treatment on genome-wide expression profiling in the rat striatum was analysed and compared to the pathological expression profile in Q175 knock-in (Q175 KI) vs Q25 WT mouse models. A broad, unbiased pathway analysis was conducted, followed by testing the enrichment of relevant pathways. Pridopidine upregulated the BDNF pathway (P = 1.73E-10), and its effect on BDNF secretion was sigma 1 receptor (S1R) dependent. Many of the same genes were independently found to be downregulated in Q175 KI mice compared to WT (5.2e-7 < P < 0.04). In addition, pridopidine treatment upregulated the glucocorticoid receptor (GR) response, D1R-associated genes and the AKT/PI3K pathway (P = 1E-10, P = 0.001, P = 0.004, respectively). Pridopidine upregulates expression of BDNF, D1R, GR and AKT/PI3K pathways, known to promote neuronal plasticity and survival, as well as reported to demonstrate therapeutic benefit in HD animal models. Activation of S1R, necessary for its effect on the BDNF pathway, represents a core component of the mode of action of pridopidine. Since the newly identified pathways are downregulated in neurodegenerative diseases, including HD, these findings suggest that pridopidine may exert neuroprotective effects beyond its role in alleviating some symptoms of HD.

Navigating the Frontiers of Machine Learning in Neurodegenerative Disease Therapeutics.

Recent advances in machine learning hold tremendous potential for enhancing the way we develop new medicines. Over the years, machine learning has been adopted in nearly all facets of drug discovery, including patient stratification, lead discovery, biomarker development, and clinical trial design. In this review, we will discuss the latest developments linking machine learning and CNS drug discovery. While machine learning has aided our understanding of chronic diseases like Alzheimer's disease and Parkinson's disease, only modest effective therapies currently exist. We highlight promising new efforts led by academia and emerging biotech companies to leverage machine learning for exploring new therapies. These approaches aim to not only accelerate drug development but to improve the detection and treatment of neurodegenerative diseases.

pS421 huntingtin modulates mitochondrial phenotypes and confers neuroprotection in an HD hiPSC model.

Huntington disease (HD) is a hereditary neurodegenerative disorder caused by mutant huntingtin (mHTT). Phosphorylation at serine-421 (pS421) of mHTT has been shown to be neuroprotective in cellular and rodent models. However, the genetic context of these models differs from that of HD patients. Here we employed human pluripotent stem cells (hiPSCs), which express endogenous full-length mHTT. Using genome editing, we generated isogenic hiPSC lines in which the S421 site in mHTT has been mutated into a phospho-mimetic aspartic acid (S421D) or phospho-resistant alanine (S421A). We observed that S421D, rather than S421A, confers neuroprotection in hiPSC-derived neural cells. Although we observed no effect of S421D on mHTT clearance or axonal transport, two aspects previously reported to be impacted by phosphorylation of mHTT at S421, our analysis revealed modulation of several aspects of mitochondrial form and function. These include mitochondrial surface area, volume, and counts, as well as improved mitochondrial membrane potential and oxidative phosphorylation. Our study validates the protective role of pS421 on mHTT and highlights a facet of the relationship between mHTT and mitochondrial changes in the context of human physiology with potential relevance to the pathogenesis of HD.

Integrity, standards, and QC-related issues with big data in pre-clinical drug discovery.

The tremendous expansion of data analytics and public and private big datasets presents an important opportunity for pre-clinical drug discovery and development. In the field of life sciences, the growth of genetic, genomic, transcriptomic and proteomic data is partly driven by a rapid decline in experimental costs as biotechnology improves throughput, scalability, and speed. Yet far too many researchers tend to underestimate the challenges and consequences involving data integrity and quality standards. Given the effect of data integrity on scientific interpretation, these issues have significant implications during preclinical drug development. We describe standardized approaches for maximizing the utility of publicly available or privately generated biological data and address some of the common pitfalls. We also discuss the increasing interest to integrate and interpret cross-platform data. Principles outlined here should serve as a useful broad guide for existing analytical practices and pipelines and as a tool for developing additional insights into therapeutics using big data.

Large-scale transcriptomic analysis reveals that pridopidine reverses aberrant gene expression and activates neuroprotective pathways in the YAC128 HD mouse.

BACKGROUND: Huntington Disease (HD) is an incurable autosomal dominant neurodegenerative disorder driven by an expansion repeat giving rise to the mutant huntingtin protein (mHtt), which is known to disrupt a multitude of transcriptional pathways. Pridopidine, a small molecule in development for treatment of HD, has been shown to improve motor symptoms in HD patients. In HD animal models, pridopidine exerts neuroprotective effects and improves behavioral and motor functions. Pridopidine binds primarily to the sigma-1 receptor, (IC50 ~ 100 nM), which mediates its neuroprotective properties, such as rescue of spine density and aberrant calcium signaling in HD neuronal cultures. Pridopidine enhances brain-derived neurotrophic factor (BDNF) secretion, which is blocked by putative sigma-1 receptor antagonist NE-100, and was shown to upregulate transcription of genes in the BDNF, glucocorticoid receptor (GR), and dopamine D1 receptor (D1R) pathways in the rat striatum. The impact of different doses of pridopidine on gene expression and transcript splicing in HD across relevant brain regions was explored, utilizing the YAC128 HD mouse model, which carries the entire human mHtt gene containing 128 CAG repeats. METHODS: RNAseq was analyzed from striatum, cortex, and hippocampus of wild-type and YAC128 mice treated with vehicle, 10 mg/kg or 30 mg/kg pridopidine from the presymptomatic stage (1.5 months of age) until 11.5 months of age in which mice exhibit progressive disease phenotypes. RESULTS: The most pronounced transcriptional effect of pridopidine at both doses was observed in the striatum with minimal effects in other regions. In addition, for the first time pridopidine was found to have a dose-dependent impact on alternative exon and junction usage, a regulatory mechanism known to be impaired in HD. In the striatum of YAC128 HD mice, pridopidine treatment initiation prior to symptomatic manifestation rescues the impaired expression of the BDNF, GR, D1R and cAMP pathways. CONCLUSIONS: Pridopidine has broad effects on restoring transcriptomic disturbances in the striatum, particularly involving synaptic transmission and activating neuroprotective pathways that are disturbed in HD. Benefits of treatment initiation at early disease stages track with trends observed in the clinic.

Early pridopidine treatment improves behavioral and transcriptional deficits in YAC128 Huntington disease mice.

Pridopidine is currently under clinical development for Huntington disease (HD), with on-going studies to better characterize its therapeutic benefit and mode of action. Pridopidine was administered either prior to the appearance of disease phenotypes or in advanced stages of disease in the YAC128 mouse model of HD. In the early treatment cohort, animals received 0, 10, or 30 mg/kg pridopidine for a period of 10.5 months. In the late treatment cohort, animals were treated for 8 weeks with 0 mg/kg or an escalating dose of pridopidine (10 to 30 mg/kg over 3 weeks). Early treatment improved motor coordination and reduced anxiety- and depressive-like phenotypes in YAC128 mice, but it did not rescue striatal and corpus callosum atrophy. Late treatment, conversely, only improved depressive-like symptoms. RNA-seq analysis revealed that early pridopidine treatment reversed striatal transcriptional deficits, upregulating disease-specific genes that are known to be downregulated during HD, a finding that is experimentally confirmed herein. This suggests that pridopidine exerts beneficial effects at the transcriptional level. Taken together, our findings support continued clinical development of pridopidine for HD, particularly in the early stages of disease, and provide valuable insight into the potential therapeutic mode of action of pridopidine.

Leveraging an NQO1 Bioactivatable Drug for Tumor-Selective Use of Poly(ADP-ribose) Polymerase Inhibitors.

Therapeutic drugs that block DNA repair, including poly(ADP-ribose) polymerase (PARP) inhibitors, fail due to lack of tumor-selectivity. When PARP inhibitors and ß-lapachone are combined, synergistic antitumor activity results from sustained NAD(P)H levels that refuel NQO1-dependent futile redox drug recycling. Significant oxygen-consumption-rate/reactive oxygen species cause dramatic DNA lesion increases that are not repaired due to PARP inhibition. In NQO1+ cancers, such as non-small-cell lung, pancreatic, and breast cancers, cell death mechanism switches from PARP1 hyperactivation-mediated programmed necrosis with ß-lapachone monotherapy to synergistic tumor-selective, caspase-dependent apoptosis with PARP inhibitors and ß-lapachone. Synergistic antitumor efficacy and prolonged survival were noted in human orthotopic pancreatic and non-small-cell lung xenograft models, expanding use and efficacy of PARP inhibitors for human cancer therapy.