Pramod C. Nair

Dimethylarginine dimethylaminohydrolase 1 (DDAH1) regulates nitric oxide (NO) synthesis, a key mediator of abnormal neovascularisation, i.e., vasculogenic mimicry, in triple negative breast cancer (TNBC). DDAH1 inhibitors have shown to suppress vasculogenic mimicry; however, they are mostly arginine-based and have limited bioavailability. In this study, we used ligand- and structure-based approaches to discover novel nonarginine-based DDAH1 inhibitors. Computational screening of over 1 million compounds led to the selection of 52 candidates for experimental testing, of which four showed more than 40% inhibition of DDAH1, with two inhibitors, A8 and C8, having IC50 values below 180 μM. In triple-negative breast cancer cells, A8 and C8 significantly suppressed vasculogenic mimicry equivalent to previously known potent DDAH1 inhibitors. Molecular dynamics simulations of A8 in complex with DDAH1 demonstrate significant remodelling of the binding pocket, leading to novel binding interactions with DDAH1 not observed with previously reported inhibitors.

Artificial intelligence (AI) has emerged as a powerful tool for solving real world problems across a wide range of industries and is increasingly being utilised by pharmaceutical companies to discover novel drug targets, biomarkers, and new drugs. Several AI-driven small molecules have entered clinical trials over the past few years, but their fate remains unknown. Currently, no commercially available compounds have been developed solely using AI approaches. In this perspective, we examine the current use of AI in drug discovery for neuropsychiatry. The pace of drug discovery in neuropsychiatric medicine has been generally sluggish, largely due to challenges such as poor pharmacological selectivity, the blood-brain barrier, and a limited understanding of disease mechanisms. AI may offer innovative solutions to these challenges. However, relative to fields such as oncology, the impact of AI on the discovery of neuropsychiatric drugs has been limited. Although novel AI-tools have been developed to overcome some of the challenges involved in neuropsychiatry drug discovery, their effectiveness has not been sufficiently evaluated. To date, innovative tools such as AlphaFold have been used to identify drug candidates for multiple neuropsychiatric conditions. AI-driven platforms have been used to study behavioural data from preclinical models to identify novel clinical candidates in clinical trials (e.g., ulotaront, phase III). It is anticipated that the availability of large-scale multi-omics data (‘big data’) will likely increase in the future, allowing us to gain a better understanding of gene-associated mechanisms in psychiatry. Using AI-based technologies such as AlphaFold, future pharmacological targets will be identified based on gene expression data, and large libraries of chemical compounds will be screened rapidly to identify novel drug candidates, resulting in shorter pre-clinical phase with lower costs.

Trace amine-associated receptor 1 (TAAR1) is an emerging pharmaceutical target for treating a variety of neuropsychiatric conditions, with several drug candidates in clinical and preclinical development. Multiple single-nucleotide variants have been associated with neuropsychiatric disorders, and genetic variants may influence the therapeutic outcomes of TAAR1-based therapies. Here, we utilize mutagenesis, functional assays, and computational models to profile schizophrenia-associated TAAR1 variant C182 ^45.50 F. In cyclic adenosine monophosphate (cAMP) assays, TAAR1 C182 ^45.50 F demonstrated a complete loss of activity in the homozygous state, and approximately 50% loss in the heterozygous state compared to TAAR1 WT (wild type). Furthermore, the surface expression of homozygous TAAR1 C182 ^45.50 F was altered, with approximately 40% reduction in surface expression compared to TAAR1 WT. Expression marginally improved in the heterozygous state. Molecular dynamics simulations of the TAAR1 C182 ^45.50 F model demonstrated increased extracellular loop 2 (ECL2) flexibility, with F182 ^45.50 forming a stable aromatic cluster (aromatic–aromatic interactions) involving F165 ^ECL2 and Y172 ^ECL2 that occludes the orthosteric binding site. The aromatic cluster is further stabilized by a transient salt bridge between E93 ^3.22 and K97 ^3.26 . Overall, our study shows the TAAR1 C182 ^45.50 F variant may disrupt endogenous trace amine signaling via a reduction of cell surface expression and occlusion of endogenous ligand binding; in addition, newly developed TAAR1 therapeutics may be subefficacious in carriers of this variant.

Supplementary Information: Figure S1 X-ray crystal structure of JNJ-77006621(ball and stick, C-atoms in green) bound to JAK2-PK domain (cartoon, yellow). Key residues are displayed in sticks [C-atoms in green near the optimisation domain (Peuleo et al. 2017, Liosi et al 2020) and C-atoms in yellow at the dimerization domain]. Supplementary Methods.

Figure showing X-ray crystal structure of pseudokinase domain inhibitor

JAK2-monomer-0ns.pdb. (Model of human JAK2 monomer used for molecular dynamics simulation).

JAK2-monomer-500ns.pdb. (Structure of human JAK2 monomer at 500 ns simulation).

Selective inhibitors of Janus kinase (JAK) 2 have been in demand since the discovery of the JAK2 V617F mutation present in patients with myeloproliferative neoplasms (MPN); however, the structural basis of V617F oncogenicity has only recently been elucidated. New structural studies reveal a role for other JAK2 domains, beyond the kinase domain, that contribute to pathogenic signaling. Here we evaluate the structure-based approaches that led to recently-approved type I JAK2 inhibitors (fedratinib and pacritinib), as well as type II (BBT594 and CHZ868) and pseudokinase inhibitors under development (JNJ7706621). With full-length JAK homodimeric structures now available, superior selective and mutation-specific JAK2 inhibitors are foreseeable.Significance:The JAK inhibitors currently used for the treatment of MPNs are effective for symptom management but not for disease eradication, primarily because they are not strongly selective for the mutant clone. The rise of computational and structure-based drug discovery approaches together with the knowledge of full-length JAK dimer complexes provides a unique opportunity to develop better targeted therapies for a range of conditions driven by pathologic JAK2 signaling.

For over seven decades, dopamine receptor 2 (D₂ receptor) antagonists remained the mainstay treatment for neuropsychiatric disorders. Although it is effective for treating hyperdopaminergic symptoms, it is often ineffective for treating negative and cognitive deficits. Trace amine-associated receptor 1 (TAAR1) is a novel, pharmacological target in the treatment of schizophrenia and other neuropsychiatric conditions. Several TAAR1 agonists are currently being developed and are in various stages of clinical and preclinical development. Previous efforts to identify TAAR1 agonists have been hampered by challenges in pharmacological characterisation, the absence of experimentally determined structures, and species-specific preferences in ligand binding and recognition. Further, poor insights into the functional selectivity of the receptor led to the characterisation of ligands with analogous signalling mechanisms. Such approaches limited the understanding of divergent receptor signalling and their potential clinical utility. Recent cryogenic electron microscopic (cryo-EM) structures of human and mouse TAAR1 (hTAAR1 and mTAAR1, respectively) in complex with agonists and G proteins have revealed detailed atomic insights into the binding pockets, binding interactions and binding modes of several agonists including endogenous trace amines (β-phenylethylamine, 3-Iodothyronamine), psychostimulants (amphetamine, methamphetamine), clinical compounds (ulotaront, ralmitaront) and repurposed drugs (fenoldopam). The in vitro screening of drug libraries has also led to the discovery of novel TAAR1 agonists (asenapine, guanabenz, guanfacine) which can be used in clinical trials or further developed to treat different neuropsychiatric conditions. Furthermore, an understanding of unappreciated signalling mechanisms (Gq, Gs/Gq) by TAAR1 agonists has come to light with the discovery of selective compounds to treat schizophrenia-like phenotypes. In this review, we discuss the emergence of structure-based approaches in the discovery of novel TAAR1 agonists through drug repurposing strategies and structure-guided designs. Additionally, we discuss the functional selectivity of TAAR1 signalling, which provides important clues for developing disorder-specific compounds.