In Vitro and In Vivo Bioequivalence Study of 3D-Printed Instant-Dissolving Levetiracetam Tablets and Subsequent Personalized Dosing for Chinese Children Based on Physiological Pharmacokinetic Modeling

Development of a physiologically based pharmacokinetic model for levetiracetam in patients with renal impairment to guide dose adjustment based on steady-state peak/trough concentrations

Levetiracetam may cause acute renal failure and myoclonic encephalopathy at high plasma levels, particularly in patients with renal impairment. The aim of this study was to develop a physiologically based pharmacokinetic (PBPK) model to predict levetiracetam pharmacokinetics in Chinese adults with epilepsy and renal impairment and define appropriate levetiracetam dosing regimen.PBPK models for healthy subjects and epilepsy patients with renal impairment were developed, validated, and adapted. Furthermore, we predicted the steady-state trough and peak concentrations of levetiracetam in patients with renal impairment using the final PBPK model, thereby recommending appropriate levetiracetam dosing regimens for different renal function stages. The predicted maximum plasma concentration (Cmax), time to maximum concentration (Tmax), area under the plasma concentration-time curve (AUC) were in agreement (0.8 ≤ fold error ≤ 1.2) with the observed, and the fold error of the trough concentrations in end-stage renal disease (ESRD) was 0.77 - 1.22. The prediction simulations indicated that the recommended doses of 1000, 750, 500, and 500 mg twice daily for epilepsy patients with mild, moderate, severe renal impairment, and ESRD, respectively, were sufficient to achieve the target plasma concentration of levetiracetam.

3D printing combined with biopredictive dissolution and PBPK/PD modeling optimization and personalization of pharmacotherapy: Are we there yet?

Precision medicine requires selecting the appropriate dosage regimen for a patient using the right drug, at the right time. Model-Informed Precision Dosing (MIPD) is a concept suggesting utilization of model-based prediction methods for optimizing the treatment benefit-harm balance, based on individual characteristics of the patient, disease, treatment method, and other factors. Here, we discuss a theoretical workflow comprising several elements, beginning from the physiologically based pharmacokinetic/pharmacodynamic (PBPK/PD) models, through 3D printed tablets with the model proposed dose, information range and flow, and the patient themselves. We also describe each of these elements, and the connection between them, highlighting challenges and potential obstacles.

3D printing for personalised medicines: implications for policy and practice

The current healthcare dynamic has shifted from one-size-fits-all to patient-centred care, with our increased understanding of pharmacokinetics and pharmacogenomics demanding a switch to more individualised therapies. As the pharmaceutical industry remains yet to succumb to the push of a technological paradigm shift, pharmacists lack the means to provide completely personalised medicine (PM) to their patients in a safe, affordable, and widely accessible manner. As additive manufacturing technology has already established its strength in producing pharmaceutical formulations, it is necessary to next consider methods by which this technology can create PM accessible from pharmacies. In this article, we reviewed the limitations of current pharmaceutical manufacturing methods for PMs, three-dimensional (3D) printing techniques that are most beneficial for PMs, implications of bringing this technology into pharmacy practice, and implications for policy surrounding 3D printing techniques in the manufacturing of PMs.

Harnessing personalized tailored medicines to digital-based data-enriched edible pharmaceuticals

Tailoring drug products to personalized medicines poses challenges for conventional dosage forms. The prominent reason is the restricted availability of flexible dosage strengths in the market. Inappropriate dosage strengths lead to adverse drug reactions or compromised therapeutic effects. The situation worsens when the drug has a narrow therapeutic window. To overcome these challenges, data-enriched edible pharmaceuticals (DEEP) are novel concepts for designing solid oral products. DEEP have individualized doses and information embedded in quick response (QR) code form. When data are presented in a QR code, the information is printed with edible ink that contains the drug in tailored doses required for the patients.

Predicting the correct dose in children: Role of computational Pediatric Physiological-based pharmacokinetics modeling tools

The pharmacokinetics (PKs) and safety of medications in particular groups can be predicted using the physiologically-based pharmacokinetic (PBPK) model. Using the PBPK model may enable safe pediatric clinical trials and speed up the process of new drug research and development, especially for children, a population in which it is relatively difficult to conduct clinical trials. This review summarizes the role of pediatric PBPK (P-PBPK) modeling software in dose prediction over the past 6 years and briefly introduces the process of general P-PBPK modeling. We summarized the theories and applications of this software and discussed the application trends and future perspectives in the area. The modeling software's extensive use will undoubtedly make it easier to predict dose prediction for young patients.