Machine Learning: A New Approach for Dose Individualization.

The application of machine learning (ML) has shown promising results in precision medicine due to its exceptional performance in dealing with complex multidimensional data. However, using ML for individualized dosing of medicines is still in its early stage, meriting further exploration. A systematic review of study designs and modeling details of using ML for individualized dosing of different drugs was performed. We have summarized the status of the study populations, predictive targets, and data sources for ML modeling, the selection of ML algorithms and features, and the evaluation and validation of their predictive performance. We also used the Prediction model Risk of Bias Assessment Tool (PROBAST) to assess the risk of bias of included studies. Currently, ML can be used for both a priori and a posteriori dose selection and optimization, and it can also assist the implementation of therapeutic drug monitoring. However, studies are mainly focused on drugs with narrow therapeutic windows, predominantly immunosuppressants (N = 23, 35.9%) and anti-infectives (N = 21, 32.8%), and there is currently only very limited attention for special populations, such as children (N = 22, 34.4%). Most studies showed poor methodological quality and a high risk of bias. The lack of external validation and clinical utility evaluation currently limits the further clinical implementation of ML for dose individualization. We therefore have proposed several ways to improve the clinical relevance of the studies and facilitate the translation of ML models into clinical practice.

A first-in-human phase 1 study of simnotrelvir, a 3CL-like protease inhibitor for treatment of COVID-19, in healthy adult subjects.

Safe and efficacious antiviral therapeutics are in urgent need for the treatment of coronavirus disease 2019. Simnotrelvir is a selective 3C-like protease inhibitor that can effectively inhibit severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). We evaluated the safety, tolerability, and pharmacokinetics of dose escalations of simnotrelvir alone or with ritonavir (simnotrelvir or simnotrelvir/ritonavir) in healthy subjects, as well as the food effect (ClinicalTrials.gov Identifier: NCT05339646). The overall incidence of adverse events (AEs) was 22.2% (17/72) and 6.3% (1/16) in intervention and placebo groups, respectively. The simnotrelvir apparent clearance was 135-369 L/h with simnotrelvir alone, and decreased significantly to 19.5-29.8 L/h with simnotrelvir/ritonavir. The simnotrelvir exposure increased in an approximately dose-proportional manner between 250 and 750 mg when co-administered with ritonavir. After consecutive twice daily dosing of simnotrelvir/ritonavir, simnotrelvir had a low accumulation index ranging from 1.39 to 1.51. The area under the curve of simnotrelvir increased 44.0 % and 47.3 % respectively, after high fat and normal diet compared with fasted status. In conclusion, simnotrelvir has adequate safety and tolerability. Its pharmacokinetics indicated a trough concentration above the level required for 90 % inhibition of SARS-CoV-2 in vitro at 750 mg/100 mg simnotrelvir/ritonavir twice daily under fasted condition, supporting further development using this dosage as the clinically recommended dose regimen.

Use of Machine Learning for Dosage Individualization of Vancomycin in Neonates.

BACKGROUND AND OBJECTIVE: High variability in vancomycin exposure in neonates requires advanced individualized dosing regimens. Achieving steady-state trough concentration (C0) and steady-state area-under-curve (AUC0-24) targets is important to optimize treatment. The objective was to evaluate whether machine learning (ML) can be used to predict these treatment targets to calculate optimal individual dosing regimens under intermittent administration conditions. METHODS: C0 were retrieved from a large neonatal vancomycin dataset. Individual estimates of AUC0-24 were obtained from Bayesian post hoc estimation. Various ML algorithms were used for model building to C0 and AUC0-24. An external dataset was used for predictive performance evaluation. RESULTS: Before starting treatment, C0 can be predicted a priori using the Catboost-based C0-ML model combined with dosing regimen and nine covariates. External validation results showed a 42.5% improvement in prediction accuracy by using the ML model compared with the population pharmacokinetic model. The virtual trial showed that using the ML optimized dose; 80.3% of the virtual neonates achieved the pharmacodynamic target (C0 in the range of 10-20 mg/L), much higher than the international standard dose (37.7-61.5%). Once therapeutic drug monitoring (TDM) measurements (C0) in patients have been obtained, AUC0-24 can be further predicted using the Catboost-based AUC-ML model combined with C0 and nine covariates. External validation results showed that the AUC-ML model can achieve an prediction accuracy of 80.3%. CONCLUSION: C0-based and AUC0-24-based ML models were developed accurately and precisely. These can be used for individual dose recommendations of vancomycin in neonates before treatment and dose revision after the first TDM result is obtained, respectively.

Developmental Population Pharmacokinetics-Pharmacodynamics of Meropenem in Chinese Neonates and Young Infants: Dosing Recommendations for Late-Onset Sepsis.

The pharmacokinetic (PK) studies of meropenem in Chinese newborns with late-onset sepsis (LOS) are still lacking. Causative pathogens of LOS and their susceptibility patterns in China differ from the data abroad. We, therefore, conducted a developmental population pharmacokinetic−pharmacodynamic analysis in Chinese newborns with the goal to optimize meropenem dosing regimens for LOS therapy. An opportunistic sampling strategy was used to collect meropenem samples, followed by model building and validation. A Monte Carlo simulation was performed to show the probability of target attainment (PTA) for various dosages. The information from 78 newborns (postmenstrual age: 27.4−46.1 weeks) was compiled and had a good fit to a 1-compartment model that had first order elimination. The median (range) values of estimated weight−normalized volume of distribution (V)and clearance (CL) were 0.60 (0.51−0.69) L/kg and 0.16 (0.04−0.51) L/h/kg, respectively. Covariate analysis revealed that postnatal age (PNA), gestational age (GA) and current weight (CW) were the most important factors in describing meropenem PK. Simulation results showed for LOS with a minimal inhibitory concentration (MIC) of 8 mg/L, the doses of 30 mg/kg 3 times daily (TID) as a 1-h infusion for newborns with GA ≤ 37 weeks and 40 mg/kg TID as a 3-h infusion for those with GA > 37 weeks were optimal, with PTA of 71.71% and 75.08%, respectively. In conclusion, we proposed an evidence-based dosing regimen of meropenem for LOS in Chinese newborns by using the population pharmacokinetic−pharmacodynamic analysis, based on domestic common pathogens and their susceptibility patterns.

Clinical utility of a model-based amoxicillin dosage regimen in neonates with early-onset sepsis.

Early-onset sepsis (EOS) is one of the most significant causes of morbidity and mortality in neonates. Currently, amoxicillin is empirically used to treat neonates with EOS. However, data on its effectiveness in neonates with EOS are still limited. Therefore, we aimed to evaluate the pharmacodynamics (PD) target attainment and effectiveness of a model-based amoxicillin dosage regimen in these neonates. We used a previously developed model and collected additional clinical data from the EOS neonates who used the model-based dosage regimen (25 mg/kg every 12 h). The primary outcomes were PD target attainment (free drug concentration above minimum inhibitory concentration during 70% of the dosing interval) and treatment failure rate. The secondary endpoints were length of amoxicillin treatment, duration of hospitalization etc. Seventy-five neonates (postmenstrual age 28.4-41.6 wk) were enrolled. A total of 70 (93.3%) neonates reached their PD target using 1 mg/L as the minimum inhibitory concentration breakpoint. The treatment failure rate was 10.7%.

Predictive Performance of Pharmacokinetic Model-Based Virtual Trials of Vancomycin in Neonates: Mathematics Matches Clinical Observation.

BACKGROUND AND OBJECTIVE: Vancomycin is frequently used to treat Gram-positive bacterial infections in neonates. However, there is still no consensus on the optimal initial dosing regimen. This study aimed to assess the performance of pharmacokinetic model-based virtual trials to predict the dose-exposure relationship of vancomycin in neonates. METHODS: The PubMed database was searched for clinical trials of vancomycin in neonates that reported the percentage of target attainment. Monte Carlo simulations were performed using nonlinear mixed-effect modeling to predict the dose-exposure relationship, and the differences in outcomes between virtual trials and real-world data in clinical studies were calculated. RESULTS: A total of 11 studies with 14 dosing groups were identified from the literature to evaluate dose-exposure relationships. For the ten dosing groups where the surrogate marker for exposure was the trough concentration, the mean ± standard deviation (SD) for the target attainment between original studies and virtual trials was 3.0 ± 7.3%. Deviations between - 10 and 10% accounted for 80% of the included dosing groups. For the other four dosing groups where the surrogate marker for exposure was concentration during continuous infusion, all deviations were between - 10 and 10%, and the mean ± SD value was 2.9 ± 4.5%. CONCLUSION: The pharmacokinetic model-based virtual trials of vancomycin exhibited good predictive performance for dose-exposure relationships in neonates. These results might be used to assist the optimization of dosing regimens in neonatal practice, avoiding the need for trial and error.

Optimal dose of meropenem for the treatment of neonatal sepsis: Dosing guideline variations and clinical practice deviations.

AIMS: Meropenem is increasingly used to treat neonatal sepsis. There are several guidelines recommending different dosing regimens of meropenem in neonates. Furthermore, deviations from these guidelines regularly occur in daily clinical practice. Therefore, the current study aimed to evaluate the variations of meropenem dosing guidelines and compare the difference between guideline and clinical practice in terms of the probability of target attainment. METHODS: This study is based on a population pharmacokinetic model. After defining the predictive performance of the model, Monte Carlo simulations were used to calculate the probability of target attainment of the currently existing dosing guidelines of meropenem and their use in daily clinical practice. RESULTS: Two guidelines and two labels were included in the Monte Carlo simulations. For 70% fT>MIC (fraction of time when the free meropenem concentration exceeded the minimum inhibitory concentration during the dosing interval), the probability of target attainment of four recommended doses ranged from 59% to 88% (MIC = 2 mg·L-1 ) and from 17% to 47% (MIC = 8 mg·L-1 ). At the clinical practice evaluation, only 20% of patients attained target exposure for the MIC of 8 mg·L-1 with 70% fT>MIC , which was much less than those found in the Food and Drug Administration labels (40%). CONCLUSION: This model-based population pharmacokinetics simulation showed that improper guidelines and/or clinical practice deviations will result in low probability of target attainment for patients infected with resistant bacteria and critically ill patients. It is important to develop and adhere to evidence-based and clinically pragmatic guidelines.

Amphiphilic small molecular mates match hydrophobic drugs to form nanoassemblies based on drug-mate strategy.

Nanomedicine has made great progress in the targeted therapy of cancer. Here, we established a novel drug-mate strategy by studying the formulation of nanodrugs at the molecular level. In the drug-mate combination, the drug is a hydrophobic drug that is poorly soluble in water, and the mate is an amphiphilic small molecule (SMA) that has both hydrophilic and lipophilic properties. We proposed that the hydrophobic drug could co-assemble with a suitable SMA on a nanoscale without additive agents. The proof-of-concept methodology and results were presented to support our hypothesis. We selected five hydrophobic drugs and more than ten amphiphilic small molecules to construct a library. Through molecular dynamic simulation and quantum chemistry computation, we speculated that the formation of nanoassemblies was related to the binding energy of the drug-mate, and the drug-mate interaction must overcome drug-drug interaction. Furthermore, the obtained SF/VECOONa nanoassemblieswas selected as a model, which had an ultra-high drug loading content (46%), improved pharmacokinetics, increased bioavailability, and enhanced therapeutic efficacy. In summary, the drug-mate strategy is an essential resource to design exact SMA for many hydrophobic drugs and provides a reference for the design of a carrier-free drug delivery system.

Optimal Sample Size for Use in Neonatal Pharmacokinetic Studies.

BACKGROUND: In recent years, population pharmacokinetics (PK) has been widely used in neonatal pharmacology. However, the sample size selection for neonatal PK studies has been highly variable and without clear consensus, especially for drugs with large individual variability. Therefore, this study's objective was to investigate the optimal sample size for use in neonatal PK studies. METHODS: A comprehensive and reliable population PK model (1631 neonates) of vancomycin was selected as a reference model. The original sparse PK dataset was divided into several sub-datasets according to different sample sizes. NONMEM was used for sub-datasets PK analysis. Statistical powers were calculated to evaluate different sample sizes (> 80% was expected). RESULTS: During population clearance estimations, the average power was 40%, 85%, 100%, and 100% for sample sizes of 10, 25, 50, and 100 neonates, respectively. And the frequency of model-estimated median clearance values within ± 10% (relative errors) of target value (0.057 L/h) were 75.0%, 68.8%, 57.8%, and 35.0%, respectively. Regarding age sub-groups (postmenstrual age (PMA) < or ≥ 37 weeks) clearance estimation, a sample size of 50 was better to complete the assessment of the neonatal age sub-group even in some cases of unbalanced age distribution. CONCLUSION: A sample size of 25 neonates provided a consistent estimation of the overall population (PMA: 23.3-52.4 weeks) clearance for a drug with high individual variability using a sparse PK sampling design. A sample size of 50 was recommended to complete neonatal age sub-group assessments.

Clinical utiliy of a model-based piperacillin dose in neonates with early-onset sepsis.

AIMS: Early-onset sepsis (EOS) is a common disease in neonates with a high morbidity and mortality rate. Piperacillin/tazobactam has been used extensively and empirically for EOS treatment without clinically validated dosing regimens, although the population pharmacokinetics (PPK) of piperacillin in neonates has been reported. Therefore, we wanted to study the effectiveness and tolerance of a PPK model-based dosing regimen of piperacillin/tazobactam in EOS patients. METHODS: A prospective, single-centre, phase II clinical study of piperacillin/tazobactam in neonates with EOS was conducted. The dosing regimen (90 mg·kg-1 , q8h) was determined based on a previous piperacillin PPK model in young infants using NONMEM v7.4. The pharmacodynamics (PD) target (70%fT > MIC, free drug concentration above MIC during 70% of the dosing interval) attainment was calculated using NONMEM combined with an opportunistic sampling design. The clinical treatment data were collected. RESULTS: A total of 52 neonates were screened and 49 neonates completed their piperacillin/tazobactam treatment course and were included in this analysis. The median (range) values of postmenstrual age were 33.57 (range 26.14-41.29) weeks. Forty-seven (96%) neonates reached their PD target. Eight (16%) neonates experienced treatment failure clinically. The mean (SD, range) duration of treatment and length of hospitalization were 100.1 (62.2, 36.2-305.8) hours and 31 (30, 5-123) days. There were no obvious adverse events and no infection-related deaths occurred in the first month of life. CONCLUSIONS: A model-based dosing regimen of piperacillin/tazobactam was evaluated clinically, was tolerated well and was determined to be effective for EOS treatment.

Population pharmacokinetics-pharmacodynamics of ceftazidime in neonates and young infants: Dosing optimization for neonatal sepsis.

Ceftazidime is a third-generation cephalosporin with high activity against many pathogens. But the ambiguity and diversity of the dosing regimens in neonates and young infants impair access to effective treatment. Thus, we conducted a population pharmacokinetic study of ceftazidime in this vulnerable population and recommended a model-based dosage regimen to optimize sepsis therapy. Totally 146 neonates and young infants (gestational age (GA): 36-43.4 weeks, postnatal age (PNA): 1-81 days, current weight (CW): 900-4500 g) were enrolled based on inclusion and exclusion criteria. Ceftazidime bloods samples (203) were obtained using the opportunistic sampling strategy and determined by the high-performance liquid chromatography. The population pharmacokinetic-pharmacodynamic analysis was conducted by nonlinear mixed effects model (NONMEM). A one-compartment model with first-order elimination best described the pharmacokinetic data. Covariate analysis showed the significance of GA, PNA, and CW on developmental pharmacokinetics. Monte Carlo simulation was performed based on above covariates and minimum inhibitory concentration (MIC). In the newborns with PNA ≤ 3 days (MIC=8 mg/L), the dose regimen was 25 mg/kg twice daily (BID). For the newborns with PNA > 3 days (MIC=16 mg/L), the optimal dose was 30 mg/kg three times daily (TID) for those with GA ≤ 37 weeks and 40 mg/kg TID for those with GA > 37 weeks. Overall, on the basis of the developmental population pharmacokinetic-pharmacodynamic analysis covering the whole range of neonates and young infants, the evidence-based ceftazidime dosage regimens were proposed to optimize neonatal early-onset and late-onset sepsis therapy.

Drug Clearance in Neonates: A Combination of Population Pharmacokinetic Modelling and Machine Learning Approaches to Improve Individual Prediction.

BACKGROUND: Population pharmacokinetic evaluations have been widely used in neonatal pharmacokinetic studies, while machine learning has become a popular approach to solving complex problems in the current era of big data. OBJECTIVE: The aim of this proof-of-concept study was to evaluate whether combining population pharmacokinetic and machine learning approaches could provide a more accurate prediction of the clearance of renally eliminated drugs in individual neonates. METHODS: Six drugs that are primarily eliminated by the kidneys were selected (vancomycin, latamoxef, cefepime, azlocillin, ceftazidime, and amoxicillin) as 'proof of concept' compounds. Individual estimates of clearance obtained from population pharmacokinetic models were used as reference clearances, and diverse machine learning methods and nested cross-validation were adopted and evaluated against these reference clearances. The predictive performance of these combined methods was compared with the performance of two other predictive methods: a covariate-based maturation model and a postmenstrual age and body weight scaling model. Relative error was used to evaluate the different methods. RESULTS: The extra tree regressor was selected as the best-fit machine learning method. Using the combined method, more than 95% of predictions for all six drugs had a relative error of < 50% and the mean relative error was reduced by an average of 44.3% and 71.3% compared with the other two predictive methods. CONCLUSION: A combined population pharmacokinetic and machine learning approach provided improved predictions of individual clearances of renally cleared drugs in neonates. For a new patient treated in clinical practice, individual clearance can be predicted a priori using our model code combined with demographic data.

Population Pharmacokinetic Study of Cefathiamidine in Infants With Augmented Renal Clearance.

Objectives: Augmented renal clearance (ARC) of primarily renally eliminated antibacterial agents may result in subtherapeutic antibiotic concentrations and, as a consequence, worse clinical outcomes. Cefathiamidine is frequently used as empirical antimicrobial therapy in children with ARC, but pharmacokinetic studies in infants are lacking. This population pharmacokinetic study in infants with ARC was conducted to determine optimal dosing regimens of cefathiamidine. Methods: The population pharmacokinetics was conducted in 20 infants treated with cefathiamidine. Plasma samples of cefathiamidine were collected using opportunistic sampling, and the concentrations were detected by UPLC-MS/MS. Data analysis was performed to determine pharmacokinetic parameters and to characterize pharmacokinetic variability of cefathiamidine using nonlinear mixed effects modelling (NONMEM) software program. Results: The data (n = 36) from 20 infants (age range, 0.35-1.86 years) with ARC were fitted best with a 1-compartment model. Allometrically scaled weight and age as significant covariates influenced cefathiamidine pharmacokinetics. The median (range) values of estimated clearance and the volume of distribution were 0.22 (0.09-0.29) L/h/kg and 0.34 (0.24-0.41) L/kg, respectively. Monte Carlo simulations showed that the cefathiamidine doses of 100 mg/kg/day q12 h, 50 mg/kg/day q8 h and 75 mg/kg/day q6 h were chosen for bacteria with MIC 0.25, 0.5 and 2 mg/L, respectively. Conclusion: The population pharmacokinetic model of cefathiamidine for infants with ARC was developed. The PTA - based dosing regimens were recommended based on the final model.

Population pharmacokinetics and dosing optimization of azlocillin in neonates with early-onset sepsis: a real-world study.

OBJECTIVES: Nowadays, real-world data can be used to improve currently available dosing guidelines and to support regulatory approval of drugs for use in neonates by overcoming practical and ethical hurdles. This proof-of-concept study aimed to assess the population pharmacokinetics of azlocillin in neonates using real-world data, to make subsequent dose recommendations and to test these in neonates with early-onset sepsis (EOS). METHODS: This prospective, open-label, investigator-initiated study of azlocillin in neonates with EOS was conducted using an adaptive two-step design. First, a maturational pharmacokinetic-pharmacodynamic model of azlocillin was developed, using an empirical dosing regimen combined with opportunistic samples resulting from waste material. Second, a Phase II clinical trial (ClinicalTrials.gov: NCT03932123) of this newly developed model-based dosing regimen of azlocillin was conducted to assure optimized target attainment [free drug concentration above MIC during 70% of the dosing interval ('70% fT>MIC')] and to investigate the tolerance and safety in neonates. RESULTS: A one-compartment model with first-order elimination, using 167 azlocillin concentrations from 95 neonates (31.7-41.6 weeks postmenstrual age), incorporating current weight and renal maturation, fitted the data best. For the second step, 45 neonates (30.3-41.3 weeks postmenstrual age) were subsequently included to investigate target attainment, tolerance and safety of the pharmacokinetic-pharmacodynamic model-based dose regimen (100 mg/kg q8h). Forty-three (95.6%) neonates reached their pharmacokinetic target and only two neonates experienced adverse events (feeding intolerance and abnormal liver function), possibly related to azlocillin. CONCLUSIONS: Target attainment, tolerance and safety of azlocillin was shown in neonates with EOS using a pharmacokinetic-pharmacodynamic model developed with real-world data.

Pilot trial on the efficacy and safety of pantethine in children with pantothenate kinase-associated neurodegeneration: a single-arm, open-label study.

OBJECTIVE: This study aimed to explore the efficacy and safety of pantethine in children with pantothenate kinase-associated neurodegeneration (PKAN). METHODS: A single-arm, open-label study was conducted. All subjects received pantethine during the 24-week period of treatment. The primary endpoints were change of the Unified Parkinson's Disease Rating Scale (UPDRS) I-III and Fahn-Marsden (FM) score from baseline to week 24 after treatment. RESULTS: Fifteen children with PKAN were enrolled, and all patients completed the study. After 24 weeks of treatment with pantethine at 60 mg/kg per day, there was no difference in either UPDRS I-III (t = 0.516, P = 0.614) or FM score (t = 0.353, P = 0.729) between the baseline and W24. Whereas the rates of increase in UPDRS I-III (Z = 2.614, p = 0.009) and FM scores (Z = 2.643, p = 0.008) were slowed. Four patients (26.7%) were evaluated as "slightly improved" by doctors through blinded video assessment. Patients with lower baseline UPDRS I-III or FM scores were more likely to be improved. The quality of life of family members improved after pantethine treatment, evaluated by PedsQL TM 2.0 FIM scores, whereas the quality of life of the patients was unchanged at W24, evaluated by PedsQL TM 4.0 and PedsQL TM 3.0 NMM. Serum level of CoA was comparable between baseline and W24. There was no drug related adverse event during the study. CONCLUSIONS: Pantethine could not significantly improve motor function in children with PKAN after 24 weeks treatment, but it may delay the progression of motor dysfunction in our study. Pantethine was well-tolerated at 60 mg/kg per day. TRIAL REGISTRATION: Clinical trial registration number at www.chictr.org.cn :ChiCTR1900021076, Registered 27 January2019, the first participant was enrolled 30 September 2018, and other 14 participants were enrolled after the trial was registered.

Developmental Population Pharmacokinetics and Dosing Optimization of Cefepime in Neonates and Young Infants.

OBJECTIVE: Cefepime is used to treat severe infections in neonates. Pharmacokinetic data have only been evaluated among preterm neonates and population pharmacokinetic model lacked external validation. Hence, our aim is to obtain the population pharmacokinetic parameters of cefepime with large sampling and optimize the cefepime dosage regimen for neonatal infection based on developmental pharmacokinetics-pharmacodynamics. METHODS: Blood samples from neonates and young infants treated with cefepime were collected using the opportunistic sampling design. The concentration of cefepime was determined using high performance liquid chromatography with ultraviolet detection. The population pharmacokinetic model was established using NONMEM software. RESULTS: One hundred blood samples from eighty-five neonates were analyzed. The population pharmacokinetics of cefepime were described by a one-compartment model with first-order elimination. Covariate analysis indicated that serum creatinine concentration, postmenstrual age and current weight had significant impact on the pharmacokinetic parameters of cefepime. Monte Carlo simulation results showed that the current dosage regimen (30 mg/kg, q12 h) had a high risk of insufficient dose. For 70% of neonates to obtain a higher free drug concentration than the minimum inhibitory concentration during 70% of the dosing interval, 50 mg/kg q12 h was needed with a susceptibility breakpoint of 4 mg/l. For a minimum inhibitory concentration of 8 mg/l, 40 mg/kg q8 h was recommended for all neonates. CONCLUSION: A population pharmacokinetic model of cefepime in neonates and young infants was established. According to simulation results based on the developmental pharmacokinetics-pharmacodynamics, different dosage regimens should be given depending on pathogens and the postmenstrual age.