Browse
Submit Data
Databases
API
Help

Dataset Information

26 Views

0 Connections

0 Citations

0 Reanalyses

0 Downloads

Omics score: 0

Computational models of drug use and addiction: A review.

ABSTRACT: In this brief review, we describe current computational models of drug-use and addiction that fall into 2 broad categories: mathematically based models that rely on computational theories, and brain-based models that link computations to brain areas or circuits. Across categories, many are models of learning and decision-making, which may be compromised in addiction. Several mathematical models take predictive coding approaches, focusing on Bayesian prediction error. Other models focus on learning processes and (traditional) prediction error. Brain-based models have incorporated prefrontal cortex, basal ganglia, and the dopamine system, based on the effects of drugs on dopamine, motivation, and executive control circuits. Several models specifically describe how behavioral control may transition from habitual to goal-directed systems, consistent with computational accounts of compromised "model-based" control. Some brain-based models have linked this to the transition of behavioral control from ventral to dorsal striatum. Overall, we propose that while computational models capture some aspects of addiction and have advanced our thinking, most have focused on the effects of drug use rather than addiction per se, most have not been tested on and/or supported by human data, and few capture multiple stages and symptoms of addiction. We conclude by suggesting a path forward for computational models of addiction. (PsycInfo Database Record (c) 2020 APA, all rights reserved).

SUBMITTER: Mollick JA

PROVIDER: S-EPMC7416739 | biostudies-literature |

REPOSITORIES: biostudies-literature

ACCESS DATA

Json Xml

Similar Datasets

Medication overuse and drug addiction: a narrative review from addiction perspective.

Project description:Chronic headache is particularly prevalent in migraineurs and it can progress to a condition known as medication overuse headache (MOH). MOH is a secondary headache caused by overuse of analgesics or other medications such as triptans to abort acute migraine attacks. The worsening of headache symptoms associated with medication overuse (MO) generally ameliorates following interruption of regular medication use, although the primary headache symptoms remain unaffected. MO patients may also develop certain behaviors such as ritualized drug administration, psychological drug attachment, and withdrawal symptoms that have been suggested to correlate with drug addiction. Although several reviews have been performed on this topic, to the authors best knowledge none of them have examined this topic from the addiction point of view. Therefore, we aimed to identify features in MO and drug addiction that may correlate. We initiate the review by introducing the classes of analgesics and medications that can cause MOH and those with high risk to produce MO. We further compare differences between sensitization resulting from MO and from drug addiction, the neuronal pathways that may be involved, and the genetic susceptibility that may overlap between the two conditions. Finally, ICHD recommendations to treat MOH will be provided herein.

| S-EPMC8080402 | biostudies-literature

Volitional social interaction prevents drug addiction in rat models.

Project description:Addiction treatment has not been appreciably improved by neuroscientific research. One problem is that mechanistic studies using rodent models do not incorporate volitional social factors, which play a critical role in human addiction. Here, using rats, we introduce an operant model of choice between drugs and social interaction. Independent of sex, drug class, drug dose, training conditions, abstinence duration, social housing, or addiction score in Diagnostic & Statistical Manual IV-based and intermittent access models, operant social reward prevented drug self-administration. This protection was lessened by delay or punishment of the social reward but neither measure was correlated with the addiction score. Social-choice-induced abstinence also prevented incubation of methamphetamine craving. This protective effect was associated with activation of central amygdala PKCδ-expressing inhibitory neurons and inhibition of anterior insular cortex activity. These findings highlight the need for incorporating social factors into neuroscience-based addiction research and support the wider implantation of socially based addiction treatments.

| S-EPMC7386559 | biostudies-literature

Drug-drug interaction prediction: databases, web servers and computational models.

Project description:In clinical treatment, two or more drugs (i.e. drug combination) are simultaneously or successively used for therapy with the purpose of primarily enhancing the therapeutic efficacy or reducing drug side effects. However, inappropriate drug combination may not only fail to improve efficacy, but even lead to adverse reactions. Therefore, according to the basic principle of improving the efficacy and/or reducing adverse reactions, we should study drug-drug interactions (DDIs) comprehensively and thoroughly so as to reasonably use drug combination. In this review, we first introduced the basic conception and classification of DDIs. Further, some important publicly available databases and web servers about experimentally verified or predicted DDIs were briefly described. As an effective auxiliary tool, computational models for predicting DDIs can not only save the cost of biological experiments, but also provide relevant guidance for combination therapy to some extent. Therefore, we summarized three types of prediction models (including traditional machine learning-based models, deep learning-based models and score function-based models) proposed during recent years and discussed the advantages as well as limitations of them. Besides, we pointed out the problems that need to be solved in the future research of DDIs prediction and provided corresponding suggestions.

| S-EPMC10782925 | biostudies-literature

Drug-induced regulatory overcompensation has motivational consequences: Implications for homeostatic and allostatic models of drug addiction.

Project description:Initial administration of 60% nitrous oxide (N2O) at 21°C ambient temperature (Ta) reduces core temperature (Tc) in rats, but tolerance develops to this hypothermic effect over several administrations. After additional N2O administrations, a hyperthermic overcompensation (sign-reversal) develops such that Tc exceeds control levels during N2O inhalation. This study investigated whether rats would employ behavioral thermoregulation to facilitate, or oppose, a previously acquired hyperthermic overcompensation during N2O administration. To establish a hyperthermic sign-reversal, male Long-Evans rats (N=12) received ten 3-h administrations of 60% N2O while housed in a gas-tight, live-in, "inactive" thermal gradient (~21°C). Following the tenth N2O exposure, the thermal gradient was activated (range of 10-37°C), and rats received both a control gas session and a 60% N2O test session in counterbalanced order. Mean Tc during N2O inhalation in the inactive gradient was reliably hypothermic during the first exposure but was reliably hyperthermic by the tenth exposure. When subsequently exposed to 60% N2O in the active gradient, rats selected a cooler Ta, which blunted the hyperthermic sign-reversal and lowered Tc throughout the remainder of the N2O exposure. Thus, autonomic heat production effectors mediating the hyperthermia were opposed by a behavioral effector that promoted increased heat loss via selection of a cooler ambient temperature. These data are compatible with an allostatic model of drug addiction that suggests that dysregulatory overcompensation in the drugged-state may motivate behaviors (e.g., drug taking) that oppose the overcompensation, thereby creating a vicious cycle of escalating drug consumption and recurring dysregulation.

| S-EPMC4415621 | biostudies-literature

Contingency Management interventions for non-prescribed drug use during treatment for opiate addiction: A systematic review and meta-analysis.

Project description:Background and aimsUse of non-prescribed drugs during treatment for opiate addiction reduces treatment success, creating a need for effective interventions. This review aimed to assess the efficacy of contingency management, a behavioural treatment that uses rewards to encourage desired behaviours, for treating non-prescribed drug use during opiate addiction treatment.MethodsA systematic search of the databases Embase, PsychInfo, PsychArticles and Medline from inception to March 2015 was performed. Random effects meta-analysis tested the use of contingency management to treat the use of drugs during opiate addiction treatment, using either longest duration of abstinence (LDA) or percentage of negative samples (PNS). Random effects moderator analyses were performed for six potential moderators: drug targeted for intervention, decade in which the study was carried out, study quality, intervention duration, type of reinforcer, and form of opiate treatment.ResultsThe search returned 3860 papers; 22 studies met inclusion criteria and were meta-analysed. Follow-up data was only available for three studies, so all analyses used end of treatment data. Contingency management performed significantly better than control in reducing drug use measured using LDA (d=0.57, 95% CI: 0.42-0.72) or PNS (d=0.41) (95% CI: 0.28-0.54). This was true for all drugs other than opiates. The only significant moderator was drug targeted (LDA: Q=10.75, p=0.03).ConclusionContingency management appears to be efficacious for treating most drug use during treatment for opiate addiction. Further research is required to ascertain the full effects of moderating variables, and longer term effects.

| S-EPMC5558146 | biostudies-literature

Molecular mechanism matters: Benefits of mechanistic computational models for drug development.

Project description:Making drug development a more efficient and cost-effective process will have a transformative effect on human health. A key, yet underutilized, tool to aid in this transformation is mechanistic computational modeling. By incorporating decades of hard-won prior knowledge of molecular interactions, cellular signaling, and cellular behavior, mechanistic models can achieve a level of predictiveness that is not feasible using solely empirical characterization of drug pharmacodynamics. These models can integrate diverse types of data from cell culture and animal experiments, including high-throughput systems biology experiments, and translate the results into the context of human disease. This provides a framework for identification of new drug targets, measurable biomarkers for drug action in target tissues, and patient populations for which a drug is likely to be effective or ineffective. Additionally, mechanistic models are valuable in virtual screening of new therapeutic strategies, such as gene or cell therapy and tissue regeneration, identifying the key requirements for these approaches to succeed in a heterogeneous patient population. These capabilities, which are distinct from and complementary to those of existing drug development strategies, demonstrate the opportunity to improve success rates in the drug development pipeline through the use of mechanistic computational models.

| S-EPMC4567444 | biostudies-other

Translational models of addiction phenotypes to advance addiction pharmacotherapy.

Project description:Alcohol and substance use disorders are heterogeneous conditions with limited effective treatment options. While there have been prior attempts to classify addiction subtypes, they have not been translated into clinical practice. In an effort to better understand heterogeneity in psychiatric disorders, the National Institute for Mental Health Research Domain Criteria (RDoC) has challenged scientists to think beyond diagnostic symptoms and to consider the underlying features of psychopathology from a neuroscience-based framework. The field of addiction has grappled with this approach by considering several key constructs with the potential to capture RDoC domains. This critical review will focus on the efforts to apply translational models of addiction phenomenology in human clinical samples, including their relative strengths and weaknesses. Opportunities for forward and reverse translation are also discussed. Deep behavioral phenotyping using neuroscience-informed batteries shows promise for a better understanding of the clinical neuroscience of addiction and advancing precision medicine for alcohol and substance use disorders.

| S-EPMC10823887 | biostudies-literature

Computational models for the prediction of adverse cardiovascular drug reactions.

Project description:BACKGROUND:Predicting adverse drug reactions (ADRs) has become very important owing to the huge global health burden and failure of drugs. This indicates a need for prior prediction of probable ADRs in preclinical stages which can improve drug failures and reduce the time and cost of development thus providing efficient and safer therapeutic options for patients. Though several approaches have been put forward for in silico ADR prediction, there is still room for improvement. METHODS:In the present work, we have used machine learning based approach for cardiovascular (CV) ADRs prediction by integrating different features of drugs, biological (drug transporters, targets and enzymes), chemical (substructure fingerprints) and phenotypic (therapeutic indications and other identified ADRs), and their two and three level combinations. To recognize quality and important features, we used minimum redundancy maximum relevance approach while synthetic minority over-sampling technique balancing method was used to introduce a balance in the training sets. RESULTS:This is a rigorous and comprehensive study which involved the generation of a total of 504 computational models for 36 CV ADRs using two state-of-the-art machine-learning algorithms: random forest and sequential minimization optimization. All the models had an accuracy of around 90% and the biological and chemical features models were more informative as compared to the models generated using chemical features. CONCLUSIONS:The results obtained demonstrated that the predictive models generated in the present study were highly accurate, and the phenotypic information of the drugs played the most important role in drug ADRs prediction. Furthermore, the results also showed that using the proposed method, different drugs properties can be combined to build computational predictive models which can effectively predict potential ADRs during early stages of drug development.

| S-EPMC6530172 | biostudies-literature

Integration of Computational Docking into Anti-Cancer Drug Response Prediction Models.

Project description:Cancer is a heterogeneous disease in that tumors of the same histology type can respond differently to a treatment. Anti-cancer drug response prediction is of paramount importance for both drug development and patient treatment design. Although various computational methods and data have been used to develop drug response prediction models, it remains a challenging problem due to the complexities of cancer mechanisms and cancer-drug interactions. To better characterize the interaction between cancer and drugs, we investigate the feasibility of integrating computationally derived features of molecular mechanisms of action into prediction models. Specifically, we add docking scores of drug molecules and target proteins in combination with cancer gene expressions and molecular drug descriptors for building response models. The results demonstrate a marginal improvement in drug response prediction performance when adding docking scores as additional features, through tests on large drug screening data. We discuss the limitations of the current approach and provide the research community with a baseline dataset of the large-scale computational docking for anti-cancer drugs.

| S-EPMC10777918 | biostudies-literature

Choice to view cocaine images predicts concurrent and prospective drug use in cocaine addiction.

Project description:Identifying variables that predict drug use in treatment-seeking drug addicted individuals is a crucial research and therapeutic goal. This study tested the hypothesis that choice to view cocaine images is associated with concurrent and prospective drug use in cocaine addiction.To establish choice-concurrent drug use associations, 71 cocaine addicted subjects (43 current users and 28 treatment seekers) provided data on (A) choice to view cocaine images and affectively pleasant, unpleasant, and neutral images [collected under explicit contingencies (when choice was made between two fully visible side-by-side images) and under more probabilistic contingencies (when choice was made between pictures hidden under flipped-over cards)]; and (B) past-month cocaine and other drug use. To establish choice-prospective drug use associations, 20 of these treatment-seeking subjects were followed over the next 6 months.Baseline cocaine-related picture choice as measured by both tasks positively correlated with subjects' concurrent cocaine and other drug use as driven by the actively-using subjects. In a subsequent multiple regression analysis, choice to view cocaine images as compared with affectively pleasant images (under probabilistic contingencies) was the only predictor that continued to be significantly associated with drug use. Importantly, this same baseline cocaine>pleasant probabilistic choice also predicted the number of days drugs were used (cocaine, alcohol, and marijuana) over the next 6 months.Simulated cocaine choice - especially when probabilistic and when compared with other positive reinforcers - may provide a valid laboratory marker of current and future drug use in cocaine addiction.

| S-EPMC3609942 | biostudies-literature

OmicsDI is part of the ELIXIR infrastructure

OmicsDI is an Elixir interoperability service. Learn more ›

Tweets

OmicsDI Databases

PRIDE
PeptideAtlas
MassIVE
JPOST Repository
Physiome Model Repository

EGA
EVA
ENA
LINCS
PAXDB
Cell Collective

MetaboLights
Metabolomics Workbench
MetabolomeExpress
GNPS
BioModels
FAIRDOMHub

ArrayExpress
dbGaP
ExpressionAtlas
GEO
NODE

Information

Databases
Help
API
Contact us
Code on GitHub
Terms of use
Submit Data