Model-informed precision dosing: State of the art and future perspectives

Model-informed precision dosing (MIPD) stands as a significant development in personalized medicine to tailor drug dosing to individual patient characteristics. MIPD moves beyond traditional therapeutic drug monitoring (TDM) by integrating mathematical predictions of dosing, and considering patient-specific factors (patient characteristics, drug measurements) as well as different sources of variability. For this purpose, rigorous model qualification is required for the application of MIPD in patients. This review delves into new methods in model selection and validation, also highlighting the role of machine learning in improving MIPD, the utilization of biosensors for real-time monitoring, as well as the potential of models integrating biomarkers for efficacy or toxicity for precision dosing. The clinical evidence of TDM and MIPD is discussed for various medical fields including infection medicine, oncology, transplant medicine, and inflammatory bowel diseases, thereby underscoring the role of pharmacokinetics/pharmacodynamics and specific biomarkers. Further research, particularly randomized clinical trials, is warranted to corroborate the value of MIPD in enhancing patient outcomes and advancing personalized medicine.

Investigation of preclinical pharmacokinetics of N-demethylsinomenine, a potential novel analgesic candidate, using an UPLC-MS/MS quantification method

N- Demethylsinomenine (NDSM), the in vivo demethylated metabolite of sinomenine, has exhibited antinociceptive efficacy against various pain models and may become a novel drug candidate for pain management. However, no reported analytical method for quantification of N- Demethylsinomenine in a biological matrix is currently available, and the pharmacokinetic properties of N- Demethylsinomenine are unknown. In the present study, an ultra-high performance liquid chromatography with tandem mass spectrometry (UPLC-MS/MS) method for quantification of N- Demethylsinomenine in rat plasma was developed and utilized to examine the preclinical pharmacokinetic profiles of N- Demethylsinomenine. The liquid-liquid extraction using ethyl acetate as the extractant was selected to treat rat plasma samples. The mixture of 25% aqueous phase (0.35% acetic acid-10 mM ammonium acetate buffer) and 75% organic phase (acetonitrile) was chosen as the mobile phases flowing on a ZORBAX C18 column to perform the chromatographic separation. After a 6-min rapid elution, NDSM and its internal standard (IS), metronidazole, were separated successfully. The ion pairs of 316/239 and 172/128 were captured for detecting N- Demethylsinomenine and IS, respectively, using multiple reaction monitoring (MRM) under a positive electrospray ionization (ESI) mode in this mass spectrometry analysis. The standard curve met linear requirements within the concentration range from 3 to 1000 ng/mL, and the lower limit of quantification (LLOQ) was 3 ng/mL. The method was evaluated regarding precision, accuracy, recovery, matrix effect, and stability, and all the results met the criteria presented in the guidelines for validation of biological analysis method. Then the pharmacokinetic profiles of N- Demethylsinomenine in rat plasma were characterized using this validated UPLC-MS/MS method. N- Demethylsinomenine exhibited the feature of linear pharmacokinetics after intravenous (i.v.) or intragastric (i.g.) administration in rats. After i. v. bolus at three dosage levels (0.5, 1, and 2 mg/kg), N- Demethylsinomenine showed the profiles of rapid elimination with mean half-life (T_1/2Z) of 1.55-1.73 h, and extensive tissue distribution with volume of distribution (V_Z) of 5.62-8.07 L/kg. After i. g. administration at three dosage levels (10, 20, and 40 mg/kg), N- Demethylsinomenine showed the consistent peak time (T_max) of 3 h and the mean absolute bioavailability of N- Demethylsinomenine was 30.46%. These pharmacokinetics findings will aid in future drug development decisions of N- Demethylsinomenine as a potential candidate for pain analgesia.

Idiopathic Nephrotic Syndrome in Pediatrics: An Up-to-date

BACKGROUND

Idiopathic or Primary Nephrotic Syndrome (INS) is a common glomerular disease in pediatric population, characterized by proteinuria, edema and hypoalbuminemia with variable findings in renal histopathology.

OBJECTIVE

This review aims to summarize current data on the etiopathogenesis diagnosis, protocols of treatment and potential therapeutic advances in INS.

METHODS

This narrative review searched for articles on histopathology, physiopathology, genetic causes, diagnosis and treatment of INS in pediatric patients. The databases evaluated were PubMed and Scopus.

RESULTS

INS is caused by an alteration in the permeability of the glomerular filtration barrier with unknown etiology. There are several gaps in the etiopathogenesis, response to treatment and clinical course of INS that justify further investigation. Novel advances include the recent understanding of the role of podocytes in INS and the identification of genes associated with the disease. The role of immune system cells and molecules has also been investigated. The diagnosis relies on clinical findings, laboratory exams and renal histology for selected cases. The treatment is primarily based on steroids administration. In case of failure, other medications should be tried. Recent studies have also searched for novel biomarkers for diagnosis and alternative therapeutic approaches.

CONCLUSION

The therapeutic response to corticosteroids still remains the main predictive factor for the prognosis of the disease. Genetic and pharmacogenomics tools may allow the identification of cases not responsive to immunosuppressive medications.

Cyclosporine A C1.5 monitoring reflects the area under the curve in children with nephrotic syndrome: a single-center experience

BACKGROUND

The currently used single-monitoring method for drug-blood-level evaluation in cyclosporine A (CsA) treatment for nephrotic syndrome (NS) was established through hourly measurements based on adult organ transplantation. However, the pharmacokinetics may differ due to different concomitant medications, age, and conditions. This study was conducted to determine the measurement timing that best reflects the CsA area under the curve (AUC) in pediatric NS.

METHODS

This retrospective study included children aged 2-14 years who were started on CsA treatment for idiopathic NS during 2013-2020. AUC_0-4 was calculated from 7 points, before and 0.5, 1, 1.5, 2, 3, and 4 h after administration. Mean values at each timing were compared with age-dependent different drug forms. Correlation between AUC_0-4 and measurement timing was analyzed.

RESULTS

There were 13 patients (11 boys) whose median age during testing was 7.3 years, and the total number of measurements was 94. The highest timing of CsA concentrations was found in C₁ 59.6%. The content liquid used at younger ages had a faster absorption time to peak value and lower blood concentration than those of capsules. Among the significant correlations observed, AUC_0-4 and C_1.5 showed the strongest significant correlation coefficient (r = 0.93, P < 0.001).

CONCLUSION

In pediatric NS, CsA metabolism may be faster than that in previous organ transplantation. Compared with C₂, C_1.5 monitoring may result in better disease control as it can best reflect the AUC_0-4 and peak values associated with side effects, which are indicators of therapeutic efficacy.

Therapeutic Drug Monitoring and Pharmacokinetic Analysis of Cyclosporine in a Pediatric Patient with Hemophagocytic Lymphohistiocytosis Complicated by Diabetes Insipidus: A Grand Round

BACKGROUND

Hemophagocytic lymphohistiocytosis (HLH) is a rare life-threatening disease. Initial therapy is based on etoposide, dexamethasone, and cyclosporine (CSA). The pharmacokinetics (PKs) of CSA and other drugs are sometimes altered in patients with HLH complicated by diabetes insipidus (DI) but the precise mechanisms remain unknown.

METHODS

In this study, the authors present a case of a 4-year-old boy with HLH complicated by DI. CSA concentrations were determined by enzyme multiplied immunoassay technique; noncompartmental PK analysis of the plasma concentration-time data was performed using PKSolver; and linear regression analysis was performed to determine linearity of relationship between urine output and C0 levels of CSA.

RESULTS

Although C0 values of CSA were lower than the target levels, the patient was successfully treated and a good clinical outcome was achieved. Linear regression analysis showed a strong negative correlation between urine output and the serum trough concentration (C0) of CSA, pharmacokinetic analysis showed the main PK parameters of CSA as follows: C0, 50.2 mcg/L; peak concentration (Cmax), 723.4 mcg/L; area under the curve0-24, 7478.2 mcg·h/L; clearance, 0.77 L/h/kg, elimination half-life, 5.3 hours, and volume of distribution, 6.0 L/kg.

CONCLUSIONS

To the best of the authors' knowledge, this is the first report of the CSA PK profile in a patient with HLH complicated by DI. The authors suppose that a large fluid output and input leads to extensive CSA distribution. These results suggest that the monitoring of the Cmax and area under the curve of CSA might be more clinically and pharmacokinetically significant than that of C0 in patients with HLH complicated by DI. This case highlights the importance of therapeutic drug monitoring and demonstrates PK parameters of CSA in a pediatric patient with HLH complicated by DI.

Correlation between Cyclosporine Blood Levels and Area under Blood Concentration Time Curve in Iraqi Bone Marrow Transplant Patients Treated with Neoral® Oral Solution

Cyclosporine is a potent immunosuppressive drug. It has a narrow therapeutic index, and therefore the measurement of cyclosporine’s blood concentration is essential to obtain optimal therapy. Measurement of the area under the blood concentration-time curve (AUC) is reflective of total drug exposure. However, for organ transplant patients, the measurement of AUC involves many problems and difficulties. Thus, it is more clinically acceptable to use a single blood sample as a surrogate index of total drug exposure. Fifty-four adults bone marrow transplant Iraqi patients were given cyclosporine every 12 h as prophylaxis using Neoral® oral solution. Steady-state blood concentrations were monitored for each patient at zero time and then at 1, 2, 3, 4, 6, 8, 10, and at 12 h post-dosing. Cyclosporine blood levels were determined by using AXSYM automated immuno-analyzer which is a fluorescence polarization immunoassay (FPIA). The present investigation demonstrated the best correlation between C2 and the corresponding AUC0–4h and AUC0–12h compared to other concentrations. After two months of cyclosporine therapy, no unexpected biochemical changes and adverse effects were registered. It is concluded from this study that a single blood sample obtained at 2 h post-dosing (C2) and possibly at 3 h post dosing (C3) are ideal surrogate indexes for reflecting total drug exposure, and therefore may be used in clinical practice for predicting therapeutic and toxic effects of cyclosporine.

Limited sampling strategy for predicting isoniazid exposure in patients with extrapulmonary tuberculosis

WHAT IS KNOWN AND OBJECTIVE

Limited sampling strategies (LSS), using few sampling times after dosing, have been used to reliably predict the isoniazid area under the 24-hour concentration-time curve (AUC). Experience with isoniazid is very limited, and no LSS has been developed in south-Mediterranean populations. Hence, we aimed to develop an accurate and convenient LSS for predicting isoniazid AUC in Tunisian patients with extrapulmonary tuberculosis.

METHODS

Pharmacokinetic profiles consisting of six blood samples each, collected during the 24-hour dosing interval, were obtained from 25 (6 men and 19 women) Tunisian patients with extrapulmonary tuberculosis. The AUC was calculated according to the linear trapezoidal rule. The isoniazid concentrations at each sampling time were correlated by a linear regression analysis with the measured AUC. We analysed all the developed models for their ability to estimate the isoniazid AUC. Error indices including the percentage of Mean Absolute Prediction Error (%MAE) and the percentage of Root Mean Squared Prediction Error (%RMSE) were used to evaluate the predictive performance. The agreement between predicted and measured AUCs was investigated using Bland and Altman and mountain plot analyses.

RESULTS AND DISCUSSION

Among the 1-time-point estimations, the C₃ -predicted AUC showed the highest correlation with the measured one (r²  = .906, %MAE = 10.45% and %RMSE = 2.69%). For the 2-time-point estimations, the model including the C₂ and C₆ provided the highest correlation between predicted and measured isoniazid AUC (r²  = .960, %MAE = 8.02% and %RMSE = 1.75%). The C₀ /C₃ LSS model provided satisfactory correlation and agreement (r²  = .930, %MAE = 10.19% and %RMSE = 2.32%). The best multilinear regression model for predicting the full isoniazid AUC was found to be the combination of 3 time points: C₀ , C₁ and C₆ (r²  = .992, %MAE = 4.06% and %RMSE = 0.80%). The use of a 2-time-point LSS to predict AUC in our population could be sufficient. C₂ /C₆ combination has shown the best correlation but the use of the C₀ /C₃ combination could be more practical with an accurate prediction. Therapeutic drug monitoring of isoniazid based on the C₃ can be used also in daily clinical practice in view of its reliability and practicality.

WHAT IS NEW AND CONCLUSION

The LSS using C₀ and C₃ is reliable, accurate and practical to estimate the AUC of isoniazid. A 1-time-point LSS including C₃ had acceptable correlation coefficient and prediction error indicators could be used alternatively.

Strategy for the Prediction of Steady-State Exposure of Digoxin to Determine Drug-Drug Interaction Potential of Digoxin With Other Drugs in Digitalization Therapy

Digoxin, a narrow therapeutic index drug, is widely used in congestive heart failure. However, the digitalization therapy involves dose titration and can exhibit drug-drug interaction. Ctrough versus area under the plasma concentration versus time curve in a dosing interval of 24 hours (AUC0-24h) and Cmax versus AUC0-24h for digoxin were established by linear regression. The predictions of digoxin AUC0-24h values were performed using published Ctrough or Cmax with appropriate regression lines. The fold difference, defined as the quotient of the observed/predicted AUC0-24h values, was evaluated. The mean square error and root mean square error, correlation coefficient (r), and goodness of the fold prediction were used to evaluate the models. Both Ctrough versus AUC0-24h (r = 0.9215) and Cmax versus AUC0-24h models for digoxin (r = 0.7781) showed strong correlations. Approximately 93.8% of the predicted digoxin AUC0-24h values were within 0.76-fold to 1.25-fold difference for Ctrough model. In sharp contrast, the Cmax model showed larger variability with only 51.6% of AUC0-24h predictions within 0.76-1.25-fold difference. The r value for observed versus predicted AUC0-24h for Ctrough (r = 0.9551; n = 177; P < 0.001) was superior to the Cmax (r = 0.6134; n = 275; P < 0.001) model. The mean square error and root mean square error (%) for the Ctrough model were 11.95% and 16.2% as compared to 67.17% and 42.3% obtained for the Cmax model. Simple linear regression models for Ctrough/Cmax versus AUC0-24h were derived for digoxin. On the basis of statistical evaluation, Ctrough was superior to Cmax model for the prediction of digoxin AUC0-24h and can be potentially used in a prospective setting for predicting drug-drug interaction or lack of it.

Interspecies scaling of urinary excretory amounts of nine drugs belonging to different therapeutic areas with diverse chemical structures - accurate prediction of the human urinary excretory amounts

1. The human urinary excretory amounts of total drug (parent + metabolites) were predicted for nine drugs with diverse chemical structures using simple allometry. The drugs used for scaling were cephapirin, olanzapine, labetolol, carisbamate, voriconazole, tofacitinib, nevirapine, ropinirole, and cyclindole. 2. The traditional allometric scaling was attempted using Y = aW^b relationship. The corresponding predicted urinary amounts were converted into % recovery by using appropriate human dose. Appropriate statistical tests comprising of fold-difference (predicted/observed values) and error calculations (MAE and RMSE) were performed. 3. The interspecies scaling of all nine drugs tested showed excellent correlation (r > 0.9672). The predictions for eight out of nine drugs (exception was cephaphirin) were contained within 0.80-1.25 fold-differences. The MAE and RMSE were within ± 18% and 14.64%, respectively. 4. The present work supported the potential application of prospective allometry scaling to predict the urinary excretory amounts of the total drug and gauge any issues for the renal handling of the total drug.

Applicability of a Single Time Point Strategy for the Prediction of Area Under the Concentration Curve of Linezolid in Patients: Superiority of Ctrough- over Cmax-Derived Linear Regression Models

Background and Objectives

Linezolid, a oxazolidinone, was the first in class to be approved for the treatment of bacterial infections arising from both susceptible and resistant strains of Gram-positive bacteria. Since overt exposure of linezolid may precipitate serious toxicity issues, therapeutic drug monitoring (TDM) may be required in certain situations, especially in patients who are prescribed other co-medications.

Methods

Using appropriate oral pharmacokinetic data (single dose and steady state) for linezolid, both maximum plasma drug concentration (C_max) versus area under the plasma concentration–time curve (AUC) and minimum plasma drug concentration (C_min) versus AUC relationship was established by linear regression models. The predictions of the AUC values were performed using published mean/median C_max or C_min data and appropriate regression lines. The quotient of observed and predicted values rendered fold difference calculation. The mean absolute error (MAE), root mean square error (RMSE), correlation coefficient (r), and the goodness of the AUC fold prediction were used to evaluate the two models.

Results

The C_max versus AUC and trough plasma concentration (C_trough) versus AUC models displayed excellent correlation, with r values of >0.9760. However, linezolid AUC values were predicted to be within the narrower boundary of 0.76 to 1.5-fold by a higher percentage by the C_trough (78.3 %) versus C_max model (48.2 %). The C_trough model showed superior correlation of predicted versus observed values and RMSE (r = 0.9031; 28.54 %, respectively) compared with the C_max model (r = 0.5824; 61.34 %, respectively).

Conclusions

A single time point strategy of using C_trough level is possible as a prospective tool to measure the AUC of linezolid in the patient population.

Interspecies scaling of excretory amounts using allometry - retrospective analysis with rifapentine, aztreonam, carumonam, pefloxacin, miloxacin, trovafloxacin, doripenem, imipenem, cefozopran, ceftazidime, linezolid for urinary excretion and rifapentine, cabotegravir, and dolutegravir for fecal excretion

1. Interspecies allometry scaling for prediction of human excretory amounts in urine or feces was performed for numerous antibacterials. Antibacterials used for urinary scaling were: rifapentine, pefloxacin, trovafloxacin (Gr1/low; <10%); miloxacin, linezolid, PNU-142300 (Gr2/medium; 10-40%); aztreonam, carumonam, cefozopran, doripenem, imipenem, and ceftazidime (Gr3/high; >50%). Rifapentine, cabotegravir, and dolutegravir was used for fecal scaling (high; >50%). 2. The employment of allometry equation: Y = aW(b) enabled scaling of urine/fecal amounts from animal species. Corresponding predicted amounts were converted into % recovery by considering the respective human dose. Comparison of predicted/observed values enabled fold difference and error calculations (mean absolute error [MAE] and root mean square error [RMSE]). Comparisons were made for urinary/fecal data; and qualitative assessment was made amongst Gr1/Gr2/Gr3 for urine. 3. Average correlation coefficient for the allometry scaling was >0.995. Excretory amount predictions were largely within 0.75- to 1.5-fold differences. Average MAE and RMSE were within ±22% and 23%, respectively. Although robust predictions were achieved for higher urinary/fecal excretion (>50%), interspecies scaling was applicable for low/medium excretory drugs. 4. Based on the data, interspecies scaling of urine or fecal excretory amounts may be potentially used as a tool to understand the significance of either urinary or fecal routes of elimination in humans in early development.

Prediction of micafungin area under the curve data by using peak concentration: applicability and utility in antifungal therapy

Aim: To describe a predictive model to obtain the area under the plasma concentration versus time curve (AUC) for micafungin to aid in dosing strategies in pediatric patients. Methods: Using published pharmacokinetic data a linear regression model to describe the Cmax versus AUCtau was developed. The mean absolute error prediction, root mean square error prediction along with correlation coefficient (r) and fold prediction criteria were used to evaluate the developed linear regression model for micafungin. Results: The predicted AUC for micafungin were contained within 0.5–1.5 fold difference. The mean absolute error and root mean square error for the developed model was 15 and 27%, respectively. Conclusion: The model may be used in a prospective manner for dosing decisions of micafungin in pediatric patients.