A machine learning approach to predict daptomycin exposure from two concentrations based on Monte Carlo simulations.

Daptomycin is a concentration-dependent lipopeptide antibiotic for which exposure/effect relationships have been shown. Machine learning (ML) algorithms, developed to predict the individual exposure to drugs, have shown very good performances in comparison to maximum a posteriori Bayesian estimation (MAP-BE). The aim of this work was to predict the area under the blood concentration curve (AUC) of daptomycin from two samples and a few covariates using XGBoost ML algorithm trained on Monte Carlo simulations. Five thousand one hundred fifty patients were simulated from two literature population pharmacokinetics models. Data from the first model were split into a training set (75%) and a testing set (25%). Four ML algorithms were built to learn AUC based on daptomycin blood concentration samples at pre-dose and 1 h post-dose. The XGBoost model (best ML algorithm) with the lowest root mean square error (RMSE) in a 10-fold cross-validation experiment was evaluated in both the test set and the simulations from the second population pharmacokinetic model (validation). The ML model based on the two concentrations, the differences between these concentrations, and five other covariates (sex, weight, daptomycin dose, creatinine clearance, and body temperature) yielded very good AUC estimation in the test (relative bias/RMSE = 0.43/7.69%) and validation sets (relative bias/RMSE = 4.61/6.63%). The XGBoost ML model developed allowed accurate estimation of daptomycin AUC using C0, C1h, and a few covariates and could be used for exposure estimation and dose adjustment. This ML approach can facilitate the conduct of future therapeutic drug monitoring (TDM) studies.

In Silico Pharmacokinetics Evaluation of Forgiveness for Doravirine and Rilpivirine.

BACKGROUND: This study aimed to evaluate the concentrations of rilpivirine (RLP) and doravirine (DOR) after 3 days-off using simulations from population pharmacokinetics models. METHODS: The authors conducted a series of 500 sets of 10,000 Monte Carlo simulations to examine the steady-state conditions for 2 common dosage levels: 25 mg/d for RLP and 100 mg/d for DOR. These simulations were conducted under 2 scenarios: 1 without drug cessation and another after a 3-day break. The validity of the implementation was established through a comparison of median trough concentrations (C24h) with previously reported data. Subsequently, the proportion of simulated patients with C24h and C72h after 3 days-off (C72h/3do) that exceeded the inhibitory concentration 50 (IC50), 5.2 mcg/L for DOR and 20.5 mcg/L for RLP respectively, was calculated. The inhibitory quotient (IQ) was also computed, which was 6 times IC50 for DOR and 4.5 times IC50 for RLP. Finally, nomograms were constructed to estimate the probability of having C72h/3do > IC50 or > IQ for different ranges of C24h. RESULTS: Simulated C24h median ± SD for RLP were 61.8 ± 0.4 mcg/L and for DOR 397 ± 0 mcg/L. For RLP, 99.3 ± 0.1% exceeded IC50 at C24h, 16.4 ± 0.4% at C72h/3do, and none surpassed the IQ threshold. In contrast, DOR had 100% ± 0% above IC50 at C24h, 93.6 ± 0.2% at C72h/3do, and 58.6 ± 0.5% exceeded the IQ. CONCLUSIONS: These findings suggest that treatment with DOR may offer a more forgiving therapeutic profile than RLP, given the larger proportion of patients achieving effective drug exposure with DOR. However, it is important to acknowledge a significant limitation of this study, namely, the assumption that drug concentration is a perfect surrogate for drug effectiveness.

Impact of Enterococcus faecalis Endocarditis Treatment on Risk of Relapse.

BACKGROUND: Enterococcus faecalis infective endocarditis (EFIE) is characterized by a higher frequency of relapses than other infective endocarditis. The role of the treatment on its occurrence remains poorly understood. The aim of this study was to investigate whether the antibiotic regimen could impact the risk of relapse in EFIE. MATERIALS: This was a multicenter retrospective study of patients diagnosed with definite EFIE between 2015 and 2019 in 14 French hospitals. The primary endpoint was the occurrence of relapses within the year following endocarditis diagnosis. As death was a competing risk for relapse, Fine and Gray models were used for studying risk factors and impact of treatment. RESULTS: Of the 279 patients included, 83 (29.7%) received the amoxicillin-gentamicin (A-G) combination, 114 (40.9%) amoxicillin-ceftriaxone (A-C), 63 (22.6%) A-G and A-C (A-G/A-C) sequentially, 9 (3.2%) amoxicillin (A), and 10 received other treatments. One-year-relapse rate was 9.3% (26 patients). Relapse occurred after a median delay of 107 days from EFIE diagnosis; 6 occurred after 6 months, and 6 were diagnosed by blood cultures in asymptomatic patients. In multivariate analysis, surgery during treatment was a protective factor against one-year relapse and death.The cumulative incidence of relapse 1 year after endocarditis was 46.2% for patients treated with amoxicillin, 13.4% with A-G, 14.7% with A-C, and 4.3% with A-G/A-C (P≥.05 in multivariate analysis). CONCLUSIONS: Relapses after treatment of EFIE are frequent, frequently asymptomatic, and may occur more than 6 months after the initial episode.

Multicenter Retrospective Study of Vascular Infections and Endocarditis Caused by Campylobacter spp., France.

The incidence of campylobacteriosis has substantially increased over the past decade, notably in France. Secondary localizations complicating invasive infections are poorly described. We aimed to describe vascular infection or endocarditis caused by Campylobacter spp. We included 57 patients from a nationwide 5-year retrospective study on Campylobacter spp. bacteremia conducted in France; 44 patients had vascular infections, 12 had endocarditis, and 1 had both conditions. Campylobacter fetus was the most frequently involved species (83%). Antibiotic treatment involved a ß-lactam monotherapy (54%) or was combined with a fluoroquinolone or an aminoglycoside (44%). The mortality rate was 25%. Relapse occurred in 8% of cases and was associated with delayed initiation of an efficient antimicrobial therapy after the first symptoms, diabetes, and coexistence of an osteoarticular location. Cardiovascular Campylobacter spp. infections are associated with a high mortality rate. Systematically searching for those localizations in cases of C. fetus bacteremia may be warranted.

Doravirine Exposure Decreased by Dialysis in a HIV Patient: A Grand Round.

BACKGROUND: The authors report the case of a 66-year-old male patient who was hemodialyzed 3 times per week for chronic renal failure and treated with 100 mg of doravirine once daily in combination with dolutegravir for HIV-1. No dose adjustment is required for doravirine in cases of severe renal injury, but the effect of dialysis on its exposure is poorly understood. METHODS RESULTS: Two series of 2 samples were drawn before and after 4-hour hemodialysis and showed an average doravirine concentration decrease of 48.1 ± 6.7%. The effects of hemodialysis were important, contrary to what was expected and has been previously reported. In addition, intraindividual variability was low. Nevertheless, because the concentrations reported were largely above the inhibitory concentration 50 (IC 50 ), no dose adjustment was required. CONCLUSIONS: The decrease in doravirine concentration due to hemodialysis observed in this case report was quite significant. Therefore, therapeutic drug monitoring might be recommended in certain patients undergoing doravirine treatment also on hemodialysis.

Retrospective Multicentric Study on Campylobacter spp. Bacteremia in France: The Campylobacteremia Study.

BACKGROUND: Campylobacter spp. bacteremia is a severe infection. A nationwide 5-year retrospective study was conducted to characterize its clinical features and prognostic factors. METHODS: The study included patients with Campylobacter spp. bacteremia diagnosed in 37 French hospitals participating in the surveillance network of the National Reference Center for Campylobacters and Helicobacters, from 1 January 2015 to 31 December 2019. The goal was to analyze the effects of a delay of appropriate antibiotic therapy and other risk factors on 30-day mortality rates, antibiotic resistance, patient characteristics, and prognosis according to the Campylobacter species. RESULTS: Among the 592 patients, Campylobacter jejuni and Campylobacter fetus were the most commonly identified species (in 42.9% and 42.6%, respectively). The patients were elderly (median age 68 years), and most had underlying conditions, mainly immunodepression (43.4%), hematologic cancers (25.9%), solid neoplasms (23%), and diabetes (22.3%). C. jejuni and Campylobacter coli were associated with gastrointestinal signs, and C. fetus was associated with secondary localizations. Among the 80 patients (13.5%) with secondary localizations, 12 had endocarditis, 38 vascular, 24 osteoarticular, and 9 ascitic fluid infections. The 30-day mortality rate was 11.7%, and an appropriate antibiotic treatment was independently associated with 30-day survival (odds ratio, 0.47 [95% confidence interval, .24-.93]; P = .03). The median efficient therapy initiation delay was quite short (2 days [interquartile range, 0-4 days]) but it had no significant impact on the 30-day mortality rate (P = .78). CONCLUSIONS: Campylobacter spp. bacteremia mainly occurred in elderly immunocompromised individuals with variable clinical presentations according to the species involved. Appropriate antimicrobial therapy was associated with improved 30-day survival.

A Machine Learning Algorithm to Predict the Starting Dose of Daptomycin.

BACKGROUND AND OBJECTIVE: The dosage of daptomycin is usually based on body weight. However, it has been shown that this approach yields too high an exposure in obese patients. Pharmacokinetic and pharmacodynamic indexes (PK/PD) have been proposed for daptomycin's antibacterial effect (AUC/CMI >666) and toxicity (C0 > 24.3 mg/L). We previously developed machine learning (ML) algorithms to predict starting doses based on Monte Carlo simulations. We propose a new way to perform probability of target attainment based on an ML algorithm to predict the daptomycin starting dose. METHODS: The Dvorchik model of daptomycin was implemented in the mrgsolve R package and 4950 pharmacokinetic profiles were simulated with doses ranging from 4 to 12 mg/kg. We trained and benchmarked four machine learning algorithms and selected the best to iteratively search for the optimal dose of daptomycin maximizing the event (AUC/CMI > 666 and C0 < 24.3 mg/L). The ML algorithm was evaluated in simulations and an external database of real patients in comparison with population pharmacokinetics. RESULTS: The performance of the Xgboost algorithms developed to predict the event (ROC AUC) in the training and test set were 0.762 and 0.761, respectively. The most important prediction variables were dose, creatinine clearance, body weight and sex. In the external database of real patients, the starting dose administered based on the ML algorithm significantly improved the target attainment by 7.9% (p-value = 0.02929) in comparison with the dose administered based on body weight. CONCLUSION: The developed algorithm improved the target attainment for daptomycin in comparison with weight-based dosing. We built a Shiny app to calculate the optimal starting dose.

Local and Global Protein Interactions Contribute to Residue Entrenchment in Beta-Lactamase TEM-1.

Due to their rapid evolution and their impact on healthcare, beta-lactamases, protein degrading beta-lactam antibiotics, are used as generic models of protein evolution. Therefore, we investigated the mutation effects in two distant beta-lactamases, TEM-1 and CTX-M-15. Interestingly, we found a site with a complex pattern of genetic interactions. Mutation G251W in TEM-1 inactivates the protein's function, just as the reciprocal mutation, W251G, does in CTX-M-15. The phylogenetic analysis revealed that mutation G has been entrenched in TEM-1's background: while rarely observed throughout the phylogeny, it is essential in TEM-1. Using a rescue experiment, in the TEM-1 G251W mutant, we identified sites that alleviate the deviation from G to W. While few of these mutations could potentially involve local interactions, most of them were found on distant residues in the 3D structure. Many well-known mutations that have an impact on protein stability, such as M182T, were recovered. Our results therefore suggest that entrenchment of an amino acid may rely on diffuse interactions among multiple sites, with a major impact on protein stability.