Zero-order metoprolol pharmacokinetics after therapeutic doses: severe toxicity and cardiogenic shock

Adapting physiologically-based pharmacokinetic models for machine learning applications

Both machine learning and physiologically-based pharmacokinetic models are becoming essential components of the drug development process. Integrating the predictive capabilities of physiologically-based pharmacokinetic (PBPK) models within machine learning (ML) pipelines could offer significant benefits in improving the accuracy and scope of drug screening and evaluation procedures. Here, we describe the development and testing of a self-contained machine learning module capable of faithfully recapitulating summary pharmacokinetic (PK) parameters produced by a full PBPK model, given a set of input drug-specific and regimen-specific information. Because of its widespread use in characterizing the disposition of orally administered drugs, the PBPK model chosen to demonstrate the methodology was an open-source implementation of a state-of-the-art compartmental and transit model called OpenCAT. The model was tested for drug formulations spanning a large range of solubility and absorption characteristics, and was evaluated for concordance against predictions of OpenCAT and relevant experimental data. In general, the values predicted by the ML models were within 20% of those of the PBPK model across the range of drug and formulation properties. However, summary PK parameter predictions from both the ML model and full PBPK model were occasionally poor with respect to those derived from experiments, suggesting deficiencies in the underlying PBPK model.

Extracorporeal treatment for poisoning to beta-adrenergic antagonists: systematic review and recommendations from the EXTRIP workgroup

BACKGROUND

β-adrenergic antagonists (BAAs) are used to treat cardiovascular disease such as ischemic heart disease, congestive heart failure, dysrhythmias, and hypertension. Poisoning from BAAs can lead to severe morbidity and mortality. We aimed to determine the utility of extracorporeal treatments (ECTRs) in BAAs poisoning.

METHODS

We conducted systematic reviews of the literature, screened studies, extracted data, and summarized findings following published EXTRIP methods.

RESULTS

A total of 76 studies (4 in vitro and 2 animal experiments, 1 pharmacokinetic simulation study, 37 pharmacokinetic studies on patients with end-stage kidney disease, and 32 case reports or case series) met inclusion criteria. Toxicokinetic or pharmacokinetic data were available on 334 patients (including 73 for atenolol, 54 for propranolol, and 17 for sotalol). For intermittent hemodialysis, atenolol, nadolol, practolol, and sotalol were assessed as dialyzable; acebutolol, bisoprolol, and metipranolol were assessed as moderately dialyzable; metoprolol and talinolol were considered slightly dialyzable; and betaxolol, carvedilol, labetalol, mepindolol, propranolol, and timolol were considered not dialyzable. Data were available for clinical analysis on 37 BAA poisoned patients (including 9 patients for atenolol, 9 for propranolol, and 9 for sotalol), and no reliable comparison between the ECTR cohort and historical controls treated with standard care alone could be performed. The EXTRIP workgroup recommends against using ECTR for patients severely poisoned with propranolol (strong recommendation, very low quality evidence). The workgroup offered no recommendation for ECTR in patients severely poisoned with atenolol or sotalol because of apparent balance of risks and benefits, except for impaired kidney function in which ECTR is suggested (weak recommendation, very low quality of evidence). Indications for ECTR in patients with impaired kidney function include refractory bradycardia and hypotension for atenolol or sotalol poisoning, and recurrent torsade de pointes for sotalol. Although other BAAs were considered dialyzable, clinical data were too limited to develop recommendations.

CONCLUSIONS

BAAs have different properties affecting their removal by ECTR. The EXTRIP workgroup assessed propranolol as non-dialyzable. Atenolol and sotalol were assessed as dialyzable in patients with kidney impairment, and the workgroup suggests ECTR in patients severely poisoned with these drugs when aforementioned indications are present.

Comparative research on the metabolism of metoprolol by four CYP2D6 allelic variants in vitro with LC-MS/MS

Specific study about the effect of cytochrome P450 2D6 (CYP2D6) polymorphisms on the metabolism of clinic drugs is of great significance for drug safety investigation. Here, the interaction between CYP2D6 variants (*1, *2, *10, *39) and metoprolol (MET) was intensively researched in vitro from the aspect of drug-enzyme kinetic study. To obtain quantitative data, α-hydroxymetoprolol (main metabolite of MET) was selected as an ideal analyte and an LC-MS/MS method was adopted for sample determination. Firstly, by selecting suitable internal standard and optimizing separation condition, the LC-MS/MS method was established and validated. Then, the drug-enzyme incubation system was optimized by two parameters: incubation time and amount of enzyme. Lastly, the interaction between CYP2D6 allelic variants and MET was characterized by K_m, V_max and CL_int. As a result, four CYP2D6 enzymes displayed diverse K_m or V_max towards MET and the values of CL_int showed a wide range from 8.91 to 100%. Relative to CYP2D6*1 (CL_int*1 = 100%), CYP2D6*2 demonstrated the second high catalytic activity (CL_int*2/*1 = 74.87%) while CYP2D6*39 (CL_int*39/*1 = 29.65%) and CYP2D6*10 (CL_int*10/*1 = 8.91%) showed minimal catalytic activity. This comprehensive in vitro data suggested the prominent influence of CYP2D6 polymorphisms on the metabolism of MET, which could offer valuable information for personalized administration of MET in clinic.

Pharmacogenomics of Medications Commonly Used in the Intensive Care Unit

In the intensive care unit (ICU) setting, where highly variable and insufficient drug efficacies, as well as frequent and unpredictable adverse drug reactions (ADRs) occur, pharmacogenomics (PGx) offers an opportunity to improve health outcomes. However, PGx has not been fully evaluated in the ICU, partly due to lack of knowledge of how genetic markers may affect drug therapy. To fill in this gap, we conducted a review to summarize the PGx information for the medications commonly encountered in the ICU.