Model-Informed Precision Dosing (MIPD).

Model-informed precision dosing (MIPD) is an advanced quantitative approach focusing on individualized dosage optimization, integrating complex mathematical and statistical models of drugs and disease combined with individual demographic and clinical patient characteristics [...].

Physiologically-based pharmacokinetic modelling and dosing evaluation of gentamicin in neonates using PhysPK.

Each year, infections caused around the 25% of neonatal deaths. Early empirical treatments help to reduce this mortality, although optimized dosing regimens are still lacking. The aims were to develop and validate a gentamicin physiologically-based pharmacokinetic (PBPK) model and then potentially explore dosing regimens in neonates using pharmacokinetic and pharmacodynamic criteria. The PBPK model developed consisted of 2 flow-limited tissues: kidney and other tissues. It has been implemented on a new tool called PhysPK, which allows structure reusability and evolution as predictive engine in Model-Informed Precision Dosing (MIPD). Retrospective pharmacokinetic information based on serum levels data from 47 neonates with gestational age between 32 and 39 weeks and younger than one-week postnatal age were used for model validation. The minimal PBPK model developed adequately described the gentamicin serum concentration-time profile with an average fold error nearly 1. Extended interval gentamicin dosing regimens (6 mg/kg q36h and 6 mg/kg q48h for term and preterm neonates, respectively) showed efficacy higher than 99% with toxicity lower than 10% through Monte Carlo simulation evaluations. The gentamicin minimal PBPK model developed in PhysPK from literature information, and validated in preterm and term neonates, presents adequate predictive performance and could be useful for MIPD strategies in neonates.

Population Pharmacokinetics of Valproic Acid in Pediatric and Adult Caucasian Patients.

(1) Background: The aim of this study was to explore the valproic acid (VPA) pharmacokinetic characteristics in a large population of pediatric and adult Caucasian patients and to establish a robust population pharmacokinetic (PopPK) model. (2) Methods: A total of 2527 serum VPA samples collected from 1204 patients included in a therapeutic drug monitoring program were retrospectively analyzed. Patients were randomly assigned to either a model development group or an external evaluation group. PopPK analysis was performed on 1751 samples from 776 patients with NONMEM using a nonlinear mixed-effect modelling approach. The influence of demographic, anthropometric, treatment and comedication variables on the apparent clearance (CL/F) of VPA was studied. The bootstrap method was used to evaluate the final model internally. External evaluation was carried out using 776 VPA serum samples from 368 patients. (3) Results: A one-compartment model with first-order absorption and elimination successfully described the data. The final model included total body weight, age and comedication with phenytoin, phenobarbital and carbamazepine with a significant impact on VPA elimination. Internal and external evaluations demonstrated the good predictability of the model. (4) Conclusions: A PopPK model of VPA in Caucasian patients was successfully established, which will be helpful for model-informed precision dosing approaches in clinical patient care.

External evaluation of population pharmacokinetic models of imatinib in adults diagnosed with chronic myeloid leukaemia.

AIMS: Imatinib is considered the standard first-line treatment in newly diagnosed patients with chronic-phase myeloid leukaemia (CML). Several imatinib population pharmacokinetic (popPK) models have been developed. However, their predictive performance has not been well established when extrapolated to different populations. Therefore, this study aimed to perform an external evaluation of available imatinib popPK models developed mainly in adult patients, and to evaluate the improvement in individual model-based predictions through Bayesian forecasting computed by each model at different treatment occasions. METHODS: A literature review was conducted through PubMed and Scopus to identify popPK models. Therapeutic drug monitoring data collected in adult CML patients treated with imatinib was used for external evaluation, including prediction- and simulated-based diagnostics together with Bayesian forecasting analysis. RESULTS: Fourteen imatinib popPK studies were included for model-performance evaluation. A total of 99 imatinib samples were collected from 48 adult CML patients undergoing imatinib treatment with a minimum of one plasma concentration measured at steady-state between January 2016 and December 2020. The model proposed by Petain et al showed the best performance concerning prediction-based diagnostics in the studied population. Bayesian forecasting demonstrated a significant improvement in predictive performance at the second visit. Inter-occasion variability contributed to reducing bias and improving individual model-based predictions. CONCLUSIONS: Imatinib popPK studies developed in Caucasian subjects including α1-acid glycoprotein showed the best model performance in terms of overall bias and precision. Moreover, two imatinib samples from different visits appear sufficient to reach an adequate model-based individual prediction performance trough Bayesian forecasting.

Population pharmacokinetics of a posaconazole tablet formulation in transplant adult allogeneic stem cell recipients.

BACKGROUND: Posaconazole is an antifungal agent extensively used as a prophylaxis for invasive fungal infections (IFIs) in allogeneic stem cell transplant (SCT) recipients. Low posaconazole concentrations have been associated with reduced clinical response. The aim of this study was to develop a population pharmacokinetic (popPK) model of a posaconazole tablet formulation in allogeneic SCT adult recipients for supporting model-informed precision dosing (MIPD). MATERIALS AND METHOD: Prospective observational study performed in adult allogeneic SCT recipients receiving posaconazole as prophylaxis for IFIs and followed up by a therapeutic drug monitoring (TDM) program. Posaconazole plasma concentrations were quantified using an ultra-high-performance liquid chromatography (UPLC) with UV detector. A popPK model was developed using NONMEM v.7.4.0. Deterministic and stochastic simulations were carried out with the final model to evaluate the differences across physiological variables with impact on drug exposure. RESULTS: A one-compartment model with sequential absorption (zero and first order) and first order elimination described adequately 55 posaconazole concentrations from 36 patients. Higher doses of posaconazole were found to be required by males and patients with lower values of total serum proteins. A nomogram to estimate the posaconazole daily dose based on pharmacokinetic/pharmacodynamic (PKPD) criterion for males and females for different values of total proteins was developed. CONCLUSIONS: Gender and total serum proteins have been identified as covariates influencing posaconazole CL/F in adult allogeneic SCT recipients receiving the delay-released tablet formulation. Additional studies are required to better characterize the absorption of posaconazole and implications on dosage recommendations together with potential safety concerns.

Evaluation of Current Amikacin Dosing Recommendations and Development of an Interactive Nomogram: The Role of Albumin.

This study aimed to evaluate the potential efficacy and safety of the amikacin dosage proposed by the main guidelines and to develop an interactive nomogram, especially focused on the potential impact of albumin on initial dosage recommendation. The probability of target attainment (PTA) for each of the different dosing recommendations was calculated through stochastic simulations based on pharmacokinetic/pharmacodynamic (PKPD) criteria. Large efficacy and safety differences were observed for the evaluated amikacin dosing guidelines together with a significant impact of albumin concentrations on efficacy and safety. For all recommended dosages evaluated, efficacy and safety criteria of amikacin dosage proposed were not achieved simultaneously in most of the clinical scenarios evaluated. Furthermore, a significant impact of albumin was identified: The higher is the albumin, (i) the higher will be the PTA for maximum concentration/minimum inhibitory concentration (Cmax/MIC), (ii) the lower will be the PTA for the time period with drug concentration exceeding MIC (T>MIC) and (iii) the lower will be the PTA for toxicity (minimum concentration). Thus, accounting for albumin effect might be of interest for future amikacin dosing guidelines updates. In addition, AMKnom, an amikacin nomogram builder based on PKPD criteria, has been developed and is freely available to help evaluating dosing recommendations.

Amikacin initial dosage in patients with hypoalbuminaemia: an interactive tool based on a population pharmacokinetic approach.

OBJECTIVES: To characterize amikacin population pharmacokinetics in patients with hypoalbuminaemia and to develop a model-based interactive application for amikacin initial dosage. METHODS: A population pharmacokinetic model was developed using a non-linear mixed-effects modelling approach (NONMEM) with amikacin concentration-time data collected from clinical practice (75% hypoalbuminaemic patients). Goodness-of-fit plots, minimum objective function value, prediction-corrected visual predictive check, bootstrapping, precision and bias of parameter estimates were used for model evaluation. An interactive model-based simulation tool was developed in R (Shiny and R Markdown). Cmax/MIC ratio, time above MIC and AUC/MIC were used for optimizing amikacin initial dose recommendation. Probabilities of reaching targets were calculated for the dosage proposed. RESULTS: A one-compartment model with first-order linear elimination best described the 873 amikacin plasma concentrations available from 294 subjects (model development and external validation groups). Estimated amikacin population pharmacokinetic parameters were CL (L/h) = 0.525 + 4.78 × (CKD-EPI/98) × (0.77 × vancomycin) and V (L) = 26.3 × (albumin/2.9)-0.51 × [1 + 0.006 × (weight - 70)], where CKD-EPI is calculated with the Chronic Kidney Disease Epidemiology Collaboration equation. AMKdose is a useful interactive model-based application for a priori optimization of amikacin dosage, using individual patient and microbiological information together with predefined pharmacokinetic/pharmacodynamic (PKPD) targets. CONCLUSIONS: Serum albumin, total bodyweight, estimated glomerular filtration rate (using the CKD-EPI equation) and co-medication with vancomycin showed a significant impact on amikacin pharmacokinetics. A powerful interactive initial dose-finding tool has been developed and is freely available online. AMKdose could be useful for guiding initial amikacin dose selection before any individual pharmacokinetic information is available.

Biomarkers of disease activity and other factors as predictors of adalimumab pharmacokinetics in inflammatory bowel disease.

Inflammatory bowel disease (IBD) is commonly treated with adalimumab. The main objective of the study was to develop a population pharmacokinetic model of adalimumab in IBD patients evaluating the potential biomarkers of disease activity and other factors and its implications in adalimumab dosing. A prospective observational study was performed in adult patients diagnosed with Crohn's disease and ulcerative colitis treated with adalimumab and following a proactive therapeutic drug monitoring of serum concentrations. Adalimumab serum concentrations (ASC) were quantified mainly prior the administration using an enzyme-linked immunosorbent assay (ELISA). A population pharmacokinetic model was developed based on 303 ASC data of 104 IBD patients using non-linear mixed effect modelling approach. Sixty-five ASC from 20 additional patients were randomly selected as an external validation group. A one-compartment model with first order absorption and elimination best describe the ASC time course. Body mass index (BMI), faecal calprotectin (FCP), unexplained decline in ASC and the specific administration pen device exhibited significant influence on apparent clearance (p-value < 0.001). FCP was the inflammatory activity biomarker showing the most relevant impact on adalimumab exposure, higher than C-reactive protein and albumin, and may be useful for adalimumab dosing adjustment. The population-based pharmacokinetic model developed adequately characterized adalimumab exposure in IBD patients. The unexplained decline in ASC, FCP, BMI and the specific administration pen device were identified as meaningful variables significantly influencing adalimumab pharmacokinetics.

Population Pharmacokinetics of Apalutamide and its Active Metabolite N-Desmethyl-Apalutamide in Healthy and Castration-Resistant Prostate Cancer Subjects.

BACKGROUND: Apalutamide is a next-generation androgen receptor inhibitor approved for treatment of subjects with high-risk, non-metastatic, castration-resistant prostate cancer (NM-CRPC). OBJECTIVE: The objective of this study was to characterize the population pharmacokinetics of apalutamide and its metabolite N-desmethyl-apalutamide in healthy male and castration-resistant prostate cancer subjects. METHODS: Plasma concentration data for apalutamide and N-desmethyl-apalutamide from 1092 subjects (seven clinical studies) receiving oral apalutamide (30-480 mg) once daily were pooled for a population pharmacokinetic analysis using a non-linear mixed-effect modelling approach. The impact of clinically relevant covariates was also assessed. RESULTS: Apalutamide absorption was rapid, and the apparent steady-state volume of distribution was large (276 L), reflecting a wide body distribution. Apalutamide was eliminated slowly, with its apparent clearance increasing from 1.31 L/h after the first dose to 2.04 L/h at steady state. No evidence of time-dependent disposition was observed for N-desmethyl-apalutamide, which was also widely distributed and slowly cleared (1.5 L/h). After 4 weeks of treatment, more than 95% of steady-state exposure of apalutamide and N-desmethyl-apalutamide was reached. At a dose of apalutamide 240 mg/day, apalutamide and N-desmethyl-apalutamide exposure exhibited 5.3- and 85.2-fold accumulation in plasma, respectively. Inter-individual variability in apalutamide apparent clearance is low (< 20%). Among the covariates evaluated, apalutamide and N-desmethyl-apalutamide exposure were statistically associated only with health status, body weight, and albumin concentration, and the effect was low (< 25%). CONCLUSIONS: A population pharmacokinetic modelling approach was successfully applied to describe the pharmacokinetics of apalutamide and N-desmethyl-apalutamide. No clinically relevant covariates were identified as predictors of apalutamide and N-desmethyl-apalutamide pharmacokinetics.

Evaluation of renal function equations to predict amikacin clearance.

Objective: To evaluate the predictive performance of eight renal function equations to describe amikacin elimination in a large standard population with a wide range of age. Methods: Retrospective study of adult hospitalized patients treated with amikacin and monitored in the clinical pharmacokinetics laboratory of a pharmacy service. Renal function was calculated as Cockcroft-Gault with total, adjusted and ideal body weight, MDRD-4, CKD-EPI, rLM, BIS1, and FAS. One compartment model with first-order elimination, including interindividual variability on clearance and volume of distribution and combined residual error model was selected as a base structural model. A pharmaco-statistical analysis was performed following a non-linear mixed effects modeling approach (NONMEM 7.3 software). Results: 198 patients (61 years [18-93]) and 566 measured amikacin plasma concentrations were included. All the estimated glomerular filtration rate and creatinine clearance equations evaluated described properly the data. The linear relationship between clearance and glomerular filtration rate based on rLM showed a statistically significant improvement in the fit of the data. rLM must be evaluated carefully in renal failure for amikacin dose adjustment. Conclusions: Revised Lund-Malmö (rLM) and CKD-EPI showed the superior predictive performance of amikacin drug elimination comparing to all the alternative metrics evaluated.

Population pharmacokinetics of doxorubicin and doxorubicinol in patients diagnosed with non-Hodgkin's lymphoma.

AIMS: The aims of the study were: (i) to characterize the pharmacokinetics (PK) of doxorubicin (DOX) and doxorubicinol (DOXol) in patients diagnosed with non-Hodgkin's lymphoma (NHL) using a population approach; (ii) to evaluate the influence of various covariates on the PK of DOX; and (iii) to evaluate the role of DOX and DOXol exposure in haematological toxicity. METHODS: Population PK modelling (using NONMEM) was performed using DOX and DOXol plasma concentration-time data from 45 NHL patients treated with R-CHOP (rituximab, cyclophosphamide, doxorubicin, vincristine and prednisone). The influence of drug exposure on haematological toxicity was analysed using the Mann-Whitney-Wilcoxon test. RESULTS: A five-compartment model, three for DOX and two for DOXol, with first-order distribution and elimination for both entities best described the data. Population estimates for parent drug (CL) and metabolite (CLm ) clearance were 62 l h-1 and 27 l h-1 , respectively. The fraction metabolized to DOXol (Fm ) was estimated at 0.22. While bilirubin and aspartate aminotransferase showed an influence on the CL and CLm , the objective function value decrease was not statistically significant. A trend towards an association between the total area under the concentration-time curve (AUCtotal ), the area under the concentration-time curve for DOX (AUC) plus the area under the concentration-time curve for DOXol (AUCm ), and the neutropenia grade (P = 0.068) and the neutrophil counts (P = 0.089) was observed, according to an exponential relationship. CONCLUSIONS: The PK of DOX and DOXol were well characterized by the model developed, which could be used as a helpful tool to optimize the dosage of this drug. The results suggest that the main active metabolite of DOX, DOXol, is involved in the haematological toxicity of the parent drug.

Usalpharma: a cloud-based architecture to support quality assurance training processes in health area using virtual worlds.

This paper discusses how cloud-based architectures can extend and enhance the functionality of the training environments based on virtual worlds and how, from this cloud perspective, we can provide support to analysis of training processes in the area of health, specifically in the field of training processes in quality assurance for pharmaceutical laboratories, presenting a tool for data retrieval and analysis that allows facing the knowledge discovery in the happenings inside the virtual worlds.

Validation and clinical evaluation of a UHPLC method with fluorescence detector for plasma quantification of doxorubicin and doxorubicinol in haematological patients.

A rapid and simple UHPLC-fluorescence detection method for the quantification of doxorubicin and its main metabolite, doxorubicinol, in human plasma has been developed. The method was also validated for its application in therapeutic drug monitoring, a clinical approach used in the optimization of oncologic treatments. Following a single protein precipitation step, chromatographic separation was achieved using a C18 column (50mm×2.10mm, particle size 1.7µm) at 50°C with a mobile phase consisting of water (containing 0.4% triethylamine and 0.4% orthophosphoric acid)/acetonitrile (77:23, v/v). Flow rate was 0.50mL/min and fluorescence detection with an excitation wavelength of 470nm and an emission wavelength of 548nm was used. The method met the specifications of linearity, selectivity, sensitivity, accuracy, precision and stability of the FDA and EMA guidelines for the validation of bioanalytical methods. Linearity for the drug (8-3000ng/mL) and the metabolite (3-150ng/mL) was observed (R(2)>0.992) and the maximum intra-day and inter-day precision coefficients of variation were less than 14% for both. The lower limits of quantification were 8 and 3ng/mL for doxorubicin and doxorubicinol, respectively. The method was successfully applied to the quantify plasma concentrations of doxorubicin and doxorubicinol in 33 patients diagnosed with haematological malignancies in which broad ranges for drug (8.3-2766.0ng/mL) and metabolite (4.8-104.9ng/mL) levels were measured adequately.