Effect of acute alcohol consumption on lithium kinetics

Alcohol-medication interactions: A systematic review and meta-analysis of placebo-controlled trials

Alcohol and other xenobiotics may limit the therapeutic effects of medications. We aimed at investigating alcohol-medication interactions (AMI) after the exclusion of confounding effects related to other xenobiotics. We performed a systematic review and meta-analysis of controlled studies comparing the effects induced by alcohol versus placebo on pharmacodynamic and/or pharmacokinetic parameters of approved medications. Certainty in the evidence of AMI was assessed when at least 3 independent studies and at least 200 participants were available. We included 107 articles (3097 participants): for diazepam, cannabis, opioids, and methylphenidate, we found significant AMI and enough data to assign the certainty of evidence. Alcohol consumption significantly increases the peak plasma concentration of diazepam (low certainty; almost 290 participants), cannabis (high certainty; almost 650 participants), opioids (low certainty; 560 participants), and methylphenidate (moderate certainty; 290 participants). For most medications, we found some AMI but not enough data to assign them the certainty grades; for some medications, we found no differences between alcohol and placebo in any outcomes evaluated. Our results add further evidence for interactions between alcohol and certain medications after the exclusion of confounding effects related to other xenobiotics. Physicians should advise patients who use these specific medications to avoid alcohol consumption. Further studies with appropriate control groups, enough female participants to investigate sex differences, and elderly population are needed to expand our knowledge in this field. Short phrases suitable for indexing terms.

Predicting Serum Levels of Lithium-Treated Patients: A Supervised Machine Learning Approach

Routine monitoring of lithium levels is common clinical practice. This is because the lithium prediction strategies available developed by previous studies are still limited due to insufficient prediction performance. Thus, we used machine learning approaches to predict lithium concentration in a large real-world dataset. Real-world data from multicenter electronic medical records were used in different machine learning algorithms to predict: (1) whether the serum level was 0.6–1.2 mmol/L or 0.0–0.6 mmol/L (binary prediction), and (2) its concentration value (continuous prediction). We developed models from 1505 samples through 5-fold cross-validation and used 204 independent samples to test their performance by evaluating their accuracy. Moreover, we ranked the most important clinical features in different models and reconstructed three reduced models with fewer clinical features. For binary and continuous predictions, the average accuracy of these models was 0.70–0.73 and 0.68–0.75, respectively. Seven features were listed as important features related to serum lithium levels of 0.6–1.2 mmol/L or higher lithium concentration, namely older age, lower systolic blood pressure, higher daily and last doses of lithium prescription, concomitant psychotropic drugs with valproic acid and -pine drugs, and comorbid substance-related disorders. After reducing the features in the three new predictive models, the binary or continuous models still had an average accuracy of 0.67–0.74. Machine learning processes complex clinical data and provides a potential tool for predicting lithium concentration. This may help in clinical decision-making and reduce the frequency of serum level monitoring.

Therapeutic drug monitoring for drugs used in the treatment of substance-related disorders: literature review using a therapeutic drug monitoring appropriateness rating scale

BACKGROUND

The efficacy of drugs for the treatment of substance-related disorders is moderate at best. Therapeutic drug monitoring (TDM) could be an instrument to improve outcomes. Because TDM for most of those drugs is not established, the authors reviewed the literature and built a rating scale to detect the potential added value of TDM for these pharmacologic agents.

METHODS

A literature search was performed for acamprosate, bupropion, buprenorphine, clomethiazole, disulfiram, methadone, naltrexone, and varenicline. The rating scale included 22 items and was divided in five categories: efficacy, toxicity, pharmacokinetics, patient characteristics, and cost-effectiveness. Three reference substances with established TDM were similarly assessed for comparison: clozapine, lithium, and nortriptyline. The three reference substances achieved scores of 15, 12, and 14 points, respectively.

RESULTS

Drugs for treatment of substance-related disorders achieved 3 to 17 points, 17 for methadone, 11 for buprenorphine, 10 for disulfiram, also 10 for naltrexone for the indication opioid-dependence and 9 for the indication alcohol dependence as well as bupropion, 7 points for acamprosate, 6 points for clomethiazole, and 3 for varenicline.

CONCLUSIONS

It is concluded that systematic evaluation of drug- and patient-related variables with the new rating scale can estimate the appropriateness of TDM. Because their rating revealed similar scores as the three reference drugs, it is proposed that TDM should be established for bupropion, buprenorphine, disulfiram or a metabolite, methadone, and naltrexone. An objective rating of drug- and patient-related characteristics could help laboratories focus their method development on the most likely drugs to require TDM along with a thorough drug use evaluation.