An Algorithm and R Program for Fitting and Simulation of Pharmacokinetic and Pharmacodynamic Data

CPhaMAS: An online platform for pharmacokinetic data analysis based on optimized parameter fitting algorithm

BACKGROUND AND OBJECTIVE

Clinical pharmacological modeling and statistical analysis software is an essential basic tool for drug development and personalized drug therapy. The learning curve of current basic tools is steep and unfriendly to beginners. The curve is even more challenging in cases of significant individual differences or measurement errors in data, resulting in difficulties in accurately estimating pharmacokinetic parameters by existing fitting algorithms. Hence, this study aims to explore a new optimized parameter fitting algorithm that reduces the sensitivity of the model to initial values and integrate it into the CPhaMAS platform, a user-friendly online application for pharmacokinetic data analysis.

METHODS

In this study, we proposed an optimized Nelder-Mead method that reinitializes simplex vertices when trapped in local solutions and integrated it into the CPhaMAS platform. The CPhaMAS, an online platform for pharmacokinetic data analysis, includes three modules: compartment model analysis, non-compartment analysis (NCA) and bioequivalence/bioavailability (BE/BA) analysis. Our proposed CPhaMAS platform was evaluated and compared with existing WinNonlin.

RESULTS

The platform was easy to learn and did not require code programming. The accuracy investigation found that the optimized Nelder-Mead method of the CPhaMAS platform showed better accuracy (smaller mean relative error and higher R2) in two-compartment and extravascular administration models when the initial value was set to true and abnormal values (10 times larger or smaller than the true value) compared with the WinNonlin. The mean relative error of the NCA calculation parameters of CPhaMAS and WinNonlin was <0.0001 %. When calculating BE for conventional, high-variability and narrow-therapeutic drugs. The main statistical parameters of the parameters Cmax, AUCt, and AUCinf in CPhaMAS have a mean relative error of <0.01% compared to WinNonLin.

CONCLUSIONS

In summary, CPhaMAS is a user-friendly platform with relatively accurate algorithms. It is a powerful tool for analysing pharmacokinetic data for new drug development and precision medicine.

Predicting transdermal fentanyl delivery using physics-based simulations for tailored therapy based on the age

Transdermal fentanyl patches are an effective alternative to the sustained release of oral morphine for chronic pain management. Due to the narrow therapeutic range of fentanyl, the concentration of fentanyl in the blood needs to be carefully monitored. Only then can effective pain relief be achieved while avoiding adverse effects such as respiratory depression. This study developed a physics-based digital twin of a patient by implementing drug uptake, pharmacokinetics, and pharmacodynamics models. The twin was employed to predict the in-silico effect of conventional fentanyl transdermal in a 20–80-year-old virtual patient. The results show that, with increasing age, the maximum transdermal fentanyl flux and maximum concentration of fentanyl in the blood decreased by 11.4% and 7.0%, respectively. However, the results also show that as the patient's age increases, the pain relief increases by 45.2%. Furthermore, the digital twin was used to propose a tailored therapy based on the patient's age. This predesigned therapy customized the duration of applying the commercialized fentanyl patches. According to this therapy, a 20-year-old patient needs to change the patch 2.1 times more frequently than conventional therapy, which leads to 30% more pain relief and 315% more time without pain. In addition, the digital twin was updated by the patient's pain intensity feedback. Such therapy increased the patient's breathing rate while providing effective pain relief, so a safer treatment. We quantified the added value of a patient's physics-based digital twin and sketched the future roadmap for implementing such twin-assisted treatment into the clinics.

A Use of Tritium-Labeled Peat Fulvic Acids and Polyphenolic Derivatives for Designing Pharmacokinetic Experiments on Mice

Natural products (e.g., polyphenols) have been used as biologically active compounds for centuries. Still, the mechanisms of biological activity of these multicomponent systems are poorly understood due to a lack of appropriate experimental techniques. The method of tritium thermal bombardment allows for non-selective labeling and tracking of all components of complex natural systems. In this study, we applied it to label two well-characterized polyphenolic compounds, peat fulvic acid (FA-Vi18) and oxidized lignin derivative (BP-Cx-1), of predominantly hydrophilic and hydrophobic character, respectively. The identity of the labeled samples was confirmed using size exclusion chromatography. Using ultra-high resolution Fourier transform ion cyclotron resonance mass spectrometry (FT ICR MS), key differences in the molecular composition of BP-Cx-1 and FA-Vi18 were revealed. The labeled samples ([³H]-FA-Vi18 (10 mg/kg) and [³H]-BP-Cx-1 (100 mg/kg)) were administered to female BALB/c mice intravenously (i.v.) and orally. The label distribution was assessed in blood, liver, kidneys, brain, spleen, thymus, ovaries, and heart using liquid scintillation counting. Tritium label was found in all organs studied at different concentrations. For the fulvic acid sample, the largest accumulation was observed in the kidney (C_max 28.5 mg/kg and 5.6 mg/kg, respectively) for both routes. The organs of preferential accumulation of the lignin derivative were the liver (C_max accounted for 396.7 and 16.13 mg/kg for i.v. and p.o. routes, respectively) and kidney (C_max accounted for 343.3 and 17.73 mg/kg for i.v. and p.o. routes, respectively). Our results demonstrate that using the tritium labeling technique enabled successful pharmacokinetic studies on polyphenolic drugs with very different molecular compositions. It proved to be efficient for tissue distribution studies. It was also shown that the dosage of the polyphenolic drug might be lower than 10 mg/kg due to the sensitivity of the ³H detection technique.

Limited sampling strategy to predict mycophenolic acid area under the curve in pediatric patients with nephrotic syndrome: a retrospective cohort study

PURPOSE

Limited sampling strategy (LSS) is a precise and relatively convenient therapeutic drug monitoring method. We evaluated LSSs for mycophenolic acid (MPA) in children with nephrotic syndrome treated with mycophenolic mofetil (MMF) and validated the LSSs using two different approaches.

METHODS

We measured MPA plasma concentrations in 31 children using HPLC-UV method and received 37 MPA pharmacokinetic profiles (0-12 h). For six children, MPA profiles were estimated twice after two MMF doses. LSSs were developed using multilinear regression with STATISTICA and R software and validated using validation group and bootstrap method, respectively.

RESULTS

The best three time point equations included C₁, C₃, C₆ (good guess 83%, bias - 2.78%; 95% confidence interval (CI) - 9.85-0.46); C₁, C₂, C₆ (good guess 72%, bias 0.72%; 95% CI - 5.33-7.69); and C₁, C₂, C₄ (good guess 72%, bias 2.05%; 95% CI - 4.92-13.01) for STATISTICA software. For R software, the best equations consisted of C₁, C₃, C₆ (good guess 92%, bias - 2.69%; 95% CI - 27.18-33.75); C₀, C₁, C₃ (good guess 84%, bias - 2.11%; 95% CI - 24.19-22.29); and C₀, C₁, C₂ (good guess 84%, bias - 0.48%; 95% CI - 30.77-54.07). During validation, better results were obtained for R evaluations, i.e., bootstrap method.

CONCLUSIONS

The most useful equations included C₀, C₁, C₃ and C₀, C₁, C₂ time points; however, the most precise included C₁, C₃, C₆ time points because of MPA enterohepatic recirculation. Better results were obtained for bootstrap validation due to greater number of patients. Validated LSS should be used only in the population for which it was developed. As there is growing evidence that underexposure of MPA is associated with insufficient treatment response, we recommend the introduction of therapeutic drug monitoring for MPA in children with nephrotic syndrome.