LBURGINAZE™ is an engineered human arginase-1 fusion protein developed to exploit tumor metabolic vulnerabilities via systemic arginine depletion. Developing a stable formulation is necessary to preserve its enzymatic activity during long-term storage and clinical translation. Thus, this study aimed to systematically screen excipients and rationally develop aqueous and lyophilized formulations of LBURGINAZE. A panel of pharmaceutically acceptable excipients, including sugars, polyols, and amino acids, was screened under thermal stress at 25℃ by measuring arginine hydrolytic activity. Formulations were evaluated in both liquid and freeze-dried states, with lead single excipients that preserved catalytic activity subsequently incorporated into multi-component formulations. Following stability assessments, the biological potency of reconstituted LBURGINAZE was evaluated against 3D HepG2 hepatocellular carcinoma spheroids alone and in combination with the multi-kinase inhibitor sorafenib. In aqueous state, a multi-component formulation containing glycerol, L-alanine, NaCl, and polysorbate 80 provided optimal stability, preserving catalytic activity over 4 weeks. For solid-state storage, a single-excipient lyophilization containing sucrose provided superior lyoprotection, effectively maintaining 100
NONMEM is the gold-standard tool in pharmacometrics, yet much of its estimation engine operates out of sight. Routine outputs show what the engine reports, but not everything it computes along the way. The quantities needed to answer a specific question may exist transiently within the long-established Fortran-based engine, but for most modelers they remain hidden behind an implementation barrier. AI-assisted modeling changes that barrier. A user can pose a question in “vibe” language, work with an AI assistant to generate verbatim Fortran code that instruments the running engine and exposes otherwise unavailable quantities on demand for use as additional model diagnostics or in other ad hoc investigations. We illustrate this capability with two examples: capturing how subject-level samples evolve from one iteration to the next during NONMEM’s expectation-maximization (EM) methods, including importance sampling (IMP) and stochastic approximation expectation maximization (SAEM), and tracking the BFGS Hessian approximation and objective-function gradients across snapshots of first-order conditional estimation (FOCE). The examples themselves are not the central contribution. Rather, they demonstrate how AI can turn NONMEM from a closed black box into an engine that motivated users can interrogate when new questions arise. When paired with scientific judgment and numerical verification, AI can make forms of engine-level inspection once largely limited to developers and specialized experts accessible to everyday modelers, substantially shortening the distance between a NONMEM user and a NONMEM expert.
Identity (ID) testing is an integral part of the analytical control strategy of biopharmaceutical products confirming the ID of both drug substances (DS) before starting the fill-finish process as well as of the finished drug product (DP). Common methods for ID testing, including liquid chromatography–mass spectrometry and immunoassays, often involve time-intensive workflows and demanding data analysis, creating challenges and added complexity for routine quality control ID testing. This study evaluated a novel ID testing approach using nano-scale differential scanning fluorimetry (nanoDSF), which measures intrinsic protein fluorescence during thermal unfolding. Multiple data analysis strategies were evaluated, including single-point, curve correlation, and squared Euclidean distance (ED2) analyses. A panel of fifteen biopharmaceutical products was tested representing diverse molecular formats. Method performance was assessed using confusion matrices comparing samples to reference profiles. The ED2 metric (threshold 0.006) demonstrated strongest performance, achieving 100
The aim of this study was to develop a simulation model to predict the effect of gastric pH on human oral absorption of solid oral formulations containing a weakly basic drug which accounted for increases in the free drug form during tablet storage. We evaluated the in vitro solubility, permeability, and precipitation behavior of a compound and constructed a simulation model based on the experimental data. The predictive performance of the model was verified using clinical data from healthy volunteers administered capsules (prediction errors within ± 19
This study investigated the conformational equilibrium and molecular association of etoricoxib (ETR) in saturated solutions, focusing on the molecular factors that govern its organization in solution. A combination of experimental and computational methods was employed. One-dimensional and two-dimensional NMR spectroscopy, including mixing-time-dependent NOESY and DOSY measurements, were utilized to examine the conformational populations of ETR and its molecular association as a function of solvent. Quantum chemical calculations, including continuum-solvation calculations, determination of 1:1 ETR–solvent complexes, energy-decomposition analysis, and analysis using the quantum theory of atoms in molecules (QTAIM), provided supplementary qualitative models to investigate specific solute–solvent and solute–solute interaction patterns. Two main ETR conformational families were identified, with relative abundances strongly dependent on the solvent. The ETR-1/4 family predominated in DMSO-d6 and C6D6, whereas the ETR-2/3 family was predominant in CDCl3. DOSY NMR revealed significant solvent-dependent differences in the apparent hydrodynamic behavior of ETR, with the qualitative trend in molecular association observed as C6D6 < DMSO-d6 < CDCl3. Quantum-chemical calculations indicated distinct interaction patterns for the selected ETR–solvent complexes and for the crystal-derived dimer models, providing qualitative insight into the molecular interactions responsible for the observed solvent-dependent behavior. The results demonstrate significant changes in the conformational distribution and molecular association of ETR in saturated solutions as a function of solvent. The conformations observed in solution correspond to those found in the known polymorphic and solvated forms of ETR, enabling comparison between molecular arrangements in the solution phase and the solid state.
