Comparison of Compartmental and Non-Compartmental Analysis to Detect Biopharmaceutical Similarity of Intravenous Nanomaterial-Based Rifabutin Formulations

Understanding the In Vitro-In Vivo Nexus: Advanced correlation models predict clinical performance of liposomal doxorubicin

In the century of precision medicine and predictive modeling, addressing quality-related issues in the medical supply chain is critical, with 62 % of the disruptions being attributable to quality challenges. This study centers on the development and safety of liposomal doxorubicin, where animal studies alone often do not adequately explain the complex interplay between critical quality attributes and in vivo performances. Anchored in our aim to elucidate this in vitro-in vivo nexus, we compared TLD-1, a novel liposomal doxorubicin delivery system, against the established formulations Doxil® and Lipodox®. Robust in vitro-in vivo correlations (IVIVCs) with excellent coefficients of determination (R2 > 0.98) were obtained in the presence of serum under dynamic high-shear conditions. They provided the foundation for an advanced characterization and benchmarking strategy. Despite the smaller vesicle size and reduced core crystallinity of TLD-1, its release behavior closely resembled that of Doxil®. Nevertheless, subtle differences between the dosage forms observed in the in vitro setting were reflected in the bioavailabilities observed in vivo. Data from a Phase-I clinical trial facilitated the development of patient-specific IVIVCs using the physiologically-based nanocarrier biopharmaceutics model, enabling a more accurate estimation of doxorubicin exposure. This advancement could impact clinical practice by allowing for more precise dose estimation and aiding in the assessment of the interchangeability of generic liposomal doxorubicin.

A Design-Conversed Strategy Establishes the Performance Safe Space for Doxorubicin Nanosimilars

Nanomedicines exhibit multifaceted performances, yet their biopharmaceutics remain poorly understood and present several challenges in the translation from preclinical to clinical research. To address this issue and promote the production of high-quality nanomedicines, a systematic screening of the design space and in vivo performance is necessary. Establishing formulation performance specifications early on enables an informed selection of candidates and promotes the development of nanosimilars. The deconvolution of the pharmacokinetics enables the identification of key characteristics that influence their performances and disposition. Using an in vitro-in vivo rank-order relationship for doxorubicin nanoformulations, we defined in vitro release specifications for Doxil/Caelyx-like follow-on products. Additionally, our model predictions were used to establish the bioequivalence of Lipodox, a nanosimilar of Doxil/Caelyx. Furthermore, a virtual safe space was established, providing crucial insights into expected disposition kinetics and informing formulation development. By addressing bottlenecks in biopharmaceutics and formulation screening, our research advances the translation of nanomedicine from bench to bedside.

Advancing Precision Medicine: A Review of Innovative In Silico Approaches for Drug Development, Clinical Pharmacology and Personalized Healthcare

The landscape of medical treatments is undergoing a transformative shift. Precision medicine has ushered in a revolutionary era in healthcare by individualizing diagnostics and treatments according to each patient's uniquely evolving health status. This groundbreaking method of tailoring disease prevention and treatment considers individual variations in genes, environments, and lifestyles. The goal of precision medicine is to target the "five rights": the right patient, the right drug, the right time, the right dose, and the right route. In this pursuit, in silico techniques have emerged as an anchor, driving precision medicine forward and making this a realistic and promising avenue for personalized therapies. With the advancements in high-throughput DNA sequencing technologies, genomic data, including genetic variants and their interactions with each other and the environment, can be incorporated into clinical decision-making. Pharmacometrics, gathering pharmacokinetic (PK) and pharmacodynamic (PD) data, and mathematical models further contribute to drug optimization, drug behavior prediction, and drug-drug interaction identification. Digital health, wearables, and computational tools offer continuous monitoring and real-time data collection, enabling treatment adjustments. Furthermore, the incorporation of extensive datasets in computational tools, such as electronic health records (EHRs) and omics data, is also another pathway to acquire meaningful information in this field. Although they are fairly new, machine learning (ML) algorithms and artificial intelligence (AI) techniques are also resources researchers use to analyze big data and develop predictive models. This review explores the interplay of these multiple in silico approaches in advancing precision medicine and fostering individual healthcare. Despite intrinsic challenges, such as ethical considerations, data protection, and the need for more comprehensive research, this marks a new era of patient-centered healthcare. Innovative in silico techniques hold the potential to reshape the future of medicine for generations to come.