Molecular Pharmaceutics最新文献

The proceedings from the 30th August 2023 (Day 2) of the workshop "Physiologically Based Biopharmaceutics Models (PBBM) Best Practices for Drug Product Quality: Regulatory and Industry Perspectives" are provided herein. Day 2 covered PBBM case studies from six regulatory authorities which provided considerations for model verification, validation, and application based on the context of use (COU) of the model. PBBM case studies to define critical material attribute (CMA) specification settings, such as active pharmaceutical ingredient (API) particle size distributions (PSDs) were shared. PBBM case studies to define critical quality attributes (CQAs) such as the dissolution specification setting or to define the bioequivalence safe space were also discussed. Examples of PBBM using the credibility assessment framework, COU and model risk assessment, as well as scientific learnings from PBBM case studies are provided. Breakout session discussions highlighted current trends and barriers to application of PBBMs including: (a) PBBM credibility assessment framework and level of validation, (b) use of disposition parameters in PBBM and points to consider when iv data are not available, (c) conducting virtual bioequivalence trials and dealing with variability, (d) model acceptance criteria, and (e) application of PBBMs for establishing safe space and failure edges.

Computational methods including machine learning and molecular dynamics simulations have strong potential to characterize, understand, and ultimately predict the properties of proteins relevant to their stability and function as therapeutics. Such methods would streamline the development pathway by minimizing the current experimental testing required for many protein variants and formulations. The molecular understanding of thermostability and aggregation propensity has advanced significantly along with predictive algorithms based on the sequence-level or structural-level information on a protein. However, these approaches focus largely on a comparison of protein sequence variations to correlate the properties of proteins to their stability, solubility, and aggregation propensity. For therapeutic protein development, it is of equal importance to take into account the impact of the formulation conditions to elucidate and predict the stability of the antibody drugs. At the macroscopic level, changing temperature, pH, ionic strength, and the addition of excipients can significantly alter the kinetics of protein aggregation. The mechanisms controlling aggregation kinetics have been traced back to a combination of molecular features, including conformational stability, partial unfolding to aggregation-prone states, and the colloidal stability governed by surface charges and hydrophobicity. However, very little has been done to evaluate these features in the context of protein dynamics in different formulations. In this work, we have combined a range of molecular features calculated from the Fab A33 protein sequence and molecular dynamics simulations. Using the power of advanced, yet interpretable, statistical tools, it has been possible to uncover greater insights into the mechanisms behind protein stability, validating previous findings, and also develop models that can predict the aggregation kinetics within a range of 49 different solution conditions.

The particle drifting effect, where nanosized colloidal drug particles overcome the diffusional resistance of the aqueous boundary layer adjacent to the intestinal wall and increase drug absorption rates, is drawing increasing attention in pharmaceutical research. However, mechanistic understanding and accurate prediction of the particle drifting effect remain lacking. In this study, we systematically evaluated the extent of the particle drifting effect affected by drug and colloidal properties, including the size, number, and type of the moving species using biphasic diffusion experiments combined with computational fluid dynamics simulations and mass transport analyses. The results showed that the particle drifting effect is a sequential reaction of particle dissolution/dissociation in the diffusional boundary layer, followed by absorption of the free drug. Therefore, factors affecting the rate-limiting step, which can be either process or both under different circumstances, alter the particle drifting effect. Experimental results also agree with the theory that the particle dissolution rate is dependent on particle size, concentration, and drug solubility. In addition, rapid bile micelle dissociation and bile salt absorption facilitated drug absorption by the particle drifting effect. Our findings explain the highly dynamic nature of the particle drifting effect and will contribute to rational formulation development and better bioavailability prediction for formulations containing colloidal particles.

Lipid nanoparticle-encapsulated mRNA (mRNA-LNP) vaccines have been approved for use to combat coronavirus disease 2019 (COVID-19). The mRNA-LNPs contain PEG-conjugated lipids. Clinical studies have reported that mRNA-LNPs induce the production of anti-PEG antibodies, but the anti-PEG antibodies do not affect the production of neutralizing antibodies. However, the detailed influence of anti-PEG antibodies on mRNA-LNP vaccines remains unclear. Therefore, in this study, we prepared ovalbumin (OVA) as a model antigen-encoding mRNA-loaded LNP (mRNA-OVA-LNP), and we determined whether anti-PEG antibodies could affect the antigen-specific immune response of mRNA-OVA-LNP vaccination in mice pretreated with PEG-modified liposomes to induce the production of anti-PEG antibodies. After intramuscular (i.m.) injection of the mRNA-LNP, the anti-PEG antibodies did not change the expression of protein or induction of cytokine and cellular immune response but did slightly increase the induction of antigen-specific antibodies. Furthermore, repeated mRNA-LNP i.m. injection induced the production of anti-PEG IgM and anti-PEG IgG. Our results suggest that mRNA-LNP induces the production of anti-PEG antibodies, but the priming of the antigen-specific immune response of mRNA-LNP vaccination is not notably affected by anti-PEG antibodies.

Given the aging populations in advanced countries globally, many pharmaceutical companies have focused on developing central nervous system (CNS) drugs. However, due to the blood–brain barrier, drugs do not easily reach the target area in the brain. Although conventional screening methods for drug discovery involve the measurement of (unbound fraction of drug) brain-to-plasma partition coefficients, it is difficult to consider nonequilibrium between plasma and brain compound concentration–time profiles. To truly understand the pharmacokinetics/pharmacodynamics of CNS drugs, compound concentration–time profiles in the brain are necessary; however, such analyses are costly and time-consuming and require a significant number of animals. Therefore, in this study, we attempted to develop an in silico prediction method that does not require a large amount of experimental data by combining modeling and simulation (M&S) with machine learning (ML). First, we constructed a hybrid model linking plasma concentration–time profile to the brain compartment that takes into account the transit time and brain distribution of each compound. Using mouse plasma and brain time experimental values for 103 compounds, we determined the brain kinetic parameters of the hybrid model for each compound; this case was defined as scenario I (a positive control experiment) and included the full brain concentration–time profile data. Next, we built an ML model using chemical structure descriptors as explanatory variables and rate parameters as the target variable, and we then input the predicted values from 5-fold cross-validation (CV) into the hybrid model; this case was defined as scenario II, in which no brain compound concentration–time profile data exist. Finally, for scenario III, assuming that the brain concentration is obtained at only one time point, we used the brain kinetic parameters from the result of the 5-fold CV in scenario II as the initial values for the hybrid model and performed parameter refitting against the observed brain concentration at that time point. As a result, the RMSE/R2-values of the brain compound concentration–time profiles over time were 0.445/0.517 in scenario II and 0.246/0.805 in scenario III, indicating the method provides high accuracy and suggesting that it is a practical method for predicting brain compound concentration–time profiles.

The blood-brain barrier (BBB) is a highly selective network of various cell types that acts as a filter between the blood and the brain parenchyma. Because of this, the BBB remains a major obstacle for drug delivery to the central nervous system (CNS). In recent years, there has been a focus on developing various modifiable platforms, such as monoclonal antibodies (mAbs), nanobodies (Nbs), peptides, and nanoparticles, as both therapeutic agents and carriers for targeted drug delivery to treat brain cancers and diseases. Methods for bypassing the BBB can be invasive or noninvasive. Invasive techniques, such as transient disruption of the BBB using low pulse electrical fields and intracerebroventricular infusion, lack specificity and have numerous safety concerns. In this review, we will focus on noninvasive transport mechanisms that offer high levels of biocompatibility, personalization, specificity and are regarded as generally safer than their invasive counterparts. Modifiable platforms can be designed to noninvasively traverse the BBB through one or more of the following pathways: passive diffusion through a physio-pathologically disrupted BBB, adsorptive-mediated transcytosis, receptor-mediated transcytosis, shuttle-mediated transcytosis, and somatic gene transfer. Through understanding the noninvasive pathways, new applications, including Chimeric Antigen Receptors T-cell (CAR-T) therapy, and approaches for drug delivery across the BBB are emerging.

Flavonoid-based organometallic complexes were revealed to be novel bioactive compounds. The taxifolin ruthenium-p-cymene nanoparticle (TaxRu-NPs) was produced in this study, and the toxicological assessment was done prior to in vivo chemotherapeutic research. Furthermore, the in vitro chemotherapeutic investigation used the A549 and NCI-H460 lung cancer cell lines. The in vitro study found that TaxRu-NPs induced apoptosis in lung cancer cells and hindered their ability to form colonies and migrate. The in vivo study showed that treatment with TaxRu-NPs restored the histological structure of a normal lung with less hyperplasia and lymphocytic infiltration. Furthermore, the treatment downregulated the angiogenic marker VEGF and the cell survival protein β-catenin and upregulated apoptotic markers like p53 and caspase-3. TaxRu-NPs treatment additionally raised the apoptotic index and decreased cancer cell growth. Finally, TaxRu-NPs effectively alleviate lung cancer by activating p53-mediated apoptosis and preventing angiogenesis and metastasis by decreasing the VEGF/β-catenin pathway.

Heat shock protein 90 (Hsp90) is a promising target for cancer therapy and imaging. Accurate detection of Hsp90 levels in tumors via noninvasive PET imaging might be beneficial for management. To achieve this, the precursor compound Dimer-Sansalvamide A (Dimer-San A) was PEGylated and modified by conjugating it with the bifunctional chelator 1,4,7-triazacyclononane-1,4,7-triacetic acid (NOTA). The ¹⁸F-labeled PEGylated Dimer-SanA decapeptide (¹⁸F-PEGylated San A) was completed within 30 min using a two-step process. In vitro stability and specificity were assessed, including competition studies with the Hsp90 inhibitor 17-allylamino-17-demethoxygeldanamycin (17-AAG). MicroPET imaging was performed on PL45 tumor-bearing mice to evaluate probe accumulation and tumor-to-muscle ratios. Biodistribution studies determined the route of excretion. The probe resulted in a radiochemical yield of 23.11% with a purity exceeding 95%. In vitro, ¹⁸F-PEGylated San A exhibited high stability and selectively accumulated in Hsp90-positive PL45 cells, with binding effectively blocked by the Hsp90 inhibitor 17AAG, confirming its specificity. MicroPET imaging of PL45 tumor-bearing mice showed significant probe accumulation in tumor tissues at 1 and 2 h postinjection (4.06 ± 0.30 and 3.72 ± 0.61%ID/g, respectively), with optimal tumor-to-muscle ratios observed at 2 h postinjection (6.09 ± 1.92). While ¹⁸F-PEGylated San A demonstrates enhanced water solubility, as indicated by increased kidney uptake relative to liver accumulation. The study successfully incorporated PEG units to create the novel probe ¹⁸F-PEGylated San A targeting to Hsp90 without affecting its targeting capability, aimed at improving the pharmacokinetics and PET imaging of Hsp90 expression noninvasively.