Design and Synthesis of Brain Penetrant Glycopeptide Analogues of PACAP With Neuroprotective Potential for Traumatic Brain Injury and Parkinsonism

There is an unmet clinical need for curative therapies to treat neurodegenerative disorders. Most mainstay treatments currently on the market only alleviate specific symptoms and do not reverse disease progression. The Pituitary adenylate cyclase-activating polypeptide (PACAP), an endogenous neuropeptide hormone, has been extensively studied as a potential regenerative therapeutic. PACAP is widely distributed in the central nervous system (CNS) and exerts its neuroprotective and neurotrophic effects via the related Class B GPCRs PAC1, VPAC1, and VPAC2, at which the hormone shows roughly equal activity. Vasoactive intestinal peptide (VIP) also activates these receptors, and this close analogue of PACAP has also shown to promote neuronal survival in various animal models of acute and progressive neurodegenerative diseases. However, PACAP’s poor pharmacokinetic profile (non-linear PK/PD), and more importantly its limited blood-brain barrier (BBB) permeability has hampered development of this peptide as a therapeutic. We have demonstrated that glycosylation of PACAP and related peptides promotes penetration of the BBB and improves PK properties while retaining efficacy and potency in the low nanomolar range at its target receptors. Furthermore, judicious structure-activity relationship (SAR) studies revealed key motifs that can be modulated to afford compounds with diverse selectivity profiles. Most importantly, we have demonstrated that select PACAP glycopeptide analogues (2LS80Mel and 2LS98Lac) exert potent neuroprotective effects and anti-inflammatory activity in animal models of traumatic brain injury and in a mild-toxin lesion model of Parkinson’s disease, highlighting glycosylation as a viable strategy for converting endogenous peptides into robust and efficacious drug candidates.

Harnessing the Potential of Human Pluripotent Stem Cell-Derived Motor Neurons for Drug Discovery in Amyotrophic Lateral Sclerosis: From the Clinic to the Laboratory and Back to the Patient

Amyotrophic Lateral Sclerosis (ALS) is a motor neurodegenerative disorder whose cellular hallmarks are the progressive death of motor neurons (MNs) located in the anterior horn of the spinal cord, brainstem and motor cortex, and the formation of intracellular protein aggregates. Over the course of the disease, progressive paralysis takes place, leading to patient death within 3–5 years after the diagnosis. Despite decades of intensive research, only a few therapeutic options exist, with a limited benefit on the disease progression. Preclinical animal models have been very useful to decipher some aspects of the mechanisms underlying ALS. However, discoveries made using transgenic animal models have failed to translate into clinically meaningful therapeutic strategies. Thus, there is an urgent need to find solutions to discover drugs that could impact on the course of the disease, with the ultimate goal to extend the life of patients and improve their quality of life. Induced pluripotent stem cells (iPSCs), similarly to embryonic stem cells (ESCs), have the capacity to differentiate into all three embryonic germ layers, which offers the unprecedented opportunity to access patient-specific central nervous system cells in an inexhaustible manner. Human MNs generated from ALS patient iPSCs are an exciting tool for disease modelling and drug discovery projects, since they display ALS-specific phenotypes. Here, we attempted to review almost 2 decades of research in the field, first highlighting the steps required to efficiently generate MNs from human ESCs and iPSCs. Then, we address relevant ALS studies which employed human ESCs and iPSC-derived MNs that led to the identification of compounds currently being tested in clinical trials for ALS. Finally, we discuss the potential and caveats of using patient iPSC-derived MNs as a platform for drug screening, and anticipate ongoing and future challenges in ALS drug discovery.

Deep Learning Prediction of Adverse Drug Reactions in Drug Discovery Using Open TG–GATEs and FAERS Databases

Machine learning techniques are being increasingly used in the analysis of clinical and omics data. This increase is primarily due to the advancements in Artificial intelligence (AI) and the build-up of health-related big data. In this paper we have aimed at estimating the likelihood of adverse drug reactions or events (ADRs) in the course of drug discovery using various machine learning methods. We have also described a novel machine learning-based framework for predicting the likelihood of ADRs. Our framework combines two distinct datasets, drug-induced gene expression profiles from Open TG–GATEs (Toxicogenomics Project–Genomics Assisted Toxicity Evaluation Systems) and ADR occurrence information from FAERS (FDA [Food and Drug Administration] Adverse Events Reporting System) database, and can be applied to many different ADRs. It incorporates data filtering and cleaning as well as feature selection and hyperparameters fine tuning. Using this framework with Deep Neural Networks (DNN), we built a total of 14 predictive models with a mean validation accuracy of 89.4%, indicating that our approach successfully and consistently predicted ADRs for a wide range of drugs. As case studies, we have investigated the performances of our prediction models in the context of Duodenal ulcer and Hepatitis fulminant, highlighting mechanistic insights into those ADRs. We have generated predictive models to help to assess the likelihood of ADRs in testing novel pharmaceutical compounds. We believe that our findings offer a promising approach for ADR prediction and will be useful for researchers in drug discovery.