Quantitative Systems Pharmacological Analysis of Drugs of Abuse Reveals the Pleiotropy of Their Targets and the Effector Role of mTORC1

Abstract

Existing treatments against drug addiction are often ineffective due to the complexity of the networks of protein-drug and protein-protein interactions (PPIs) that mediate the development of drug addiction and related neurobiological disorders. There is an urgent need for understanding the molecular mechanisms that underlie drug addiction toward designing novel preventive or therapeutic strategies. The rapidly accumulating data on addictive drugs and their targets as well as advances in machine learning methods and computing technology now present an opportunity to systematically mine existing data and draw inferences on potential new strategies. To this aim, we carried out a comprehensive analysis of cellular pathways implicated in a diverse set of 50 drugs of abuse using quantitative systems pharmacology methods. The analysis of the drug/ligand-target interactions compiled in DrugBank and STITCH databases revealed 142 known and 48 newly predicted targets, which have been further analyzed to identify the KEGG pathways enriched at different stages of drug addiction cycle, as well as those implicated in cell signaling and regulation events associated with drug abuse. Apart from synaptic neurotransmission pathways detected as upstream signaling modules that “sense” the early effects of drugs of abuse, pathways involved in neuroplasticity are distinguished as determinants of neuronal morphological changes. Notably, many signaling pathways converge on important targets such as mTORC1. The latter emerges as a universal effector of the persistent restructuring of neurons in response to continued use of drugs of abuse.

The role of the mTOR pathway in models of drug-induced reward and the behavioural constituents of addiction

“…This conclusion also proved to be true for both mTORC1 and mTORC2 (Hoeffer and Klann, 2010). Having a role in synaptic plasticity, the mTOR network could be involved in many behavioural and cognitive processes including, to a yet to be determined extent, drug addiction (Laguesse and Ron, 2019; Neasta et al, 2014; Pei et al, 2019).…”

Recent applications of quantitative systems pharmacology and machine learning models across diseases

“…Illustrating the benefits of using ML to analyze information from databases and predict drug targets, Pei et al utilized QSP methods to provide a comprehensive study of cellular pathways involved in 50 drugs of abuse [ 125 ]. For this study, 50 drugs of abuse and their relative pharmacological actions were gathered.…”

Harnessing Human Microphysiology Systems as Key Experimental Models for Quantitative Systems Pharmacology

“…A unified signaling network generated through the chemogenomic approach (see Fig. 1a’ and Pei et al 2017, 2019) in investigation of drugs of abuse. Black arrows represent the activation, inhibition, and translocation events during signal transduction.…”

“…mTORC1 emerges as a hub where the effects on several targets of addictive drugs appear to be consolidated to lead to cell death and/or protein synthesis in the CNS and in particular AMPAR/PSD95 synthesis that induces morphological changes in the dendrites. Figure originally published in Pei et al (2019)…”

“…Thus, the cellular pathways and environment and the biological functions and processes affected by the chemicals are systematically explored. Such system-level analyses (Bian et al 2019; Pei et al 2017, 2019; Wei et al 2018; Wu et al 2019; Xu et al 2016) assist in deciphering polypharmacological effects and disease mechanisms (Fig. 2).…”