Molecular Pharmaceutics最新文献

Ferroptosis, an iron-dependent regulated cell death, has been emerging as an early mechanism in anticancer drug-induced acute kidney injury (AKI) that may benefit therapeutic intervention. However, the lack of molecular imaging methods for in vivo detection of ferroptosis restricts the early diagnosis of anticancer drug-induced AKI. Herein, we developed a PET/¹⁹F MRI dual-modal imaging probe for the monitoring of ferroptosis in AKI by chemically conjugating the Fe(II)-sensitive artemisinin (Art) motif and macrocyclic ligand 1,4,7,10-tetraazacyclododecane-1,4,7,10-tetraacetic acid (DOTA) to the CF₃-modified polyhedral oligomeric silsesquioxane (POSS) clusters, denoted as the PAD probe. The PAD probe could be converted into PA*D in the presence of Fe(II) ions and subsequently be intercepted by biological macromolecules nearby, thereby enhancing the retention effect in ferroptotic cells and tissues. After labeling with ⁶⁸Ga isotopes, the ⁶⁸Ga-labeled PAD probe in cisplatin (CDDP)-induced AKI mice displayed a significantly higher renal uptake level than that in normal mice. Moreover, the PAD probe with a precise chemical structure, relatively high ¹⁹F content, and single ¹⁹F resonance frequency allowed for interference-free and high-performance¹⁹F MRI that could detect the onset of CDDP-induced AKI at least 24 h earlier than the typical clinical/preclinical assays. Our study provides a robust dual-modal molecular imaging tool for the early diagnosis and mechanistic investigation of various ferroptosis-related diseases.

During drug discovery and development, achieving appropriate pharmacokinetics is key to establishment of the efficacy and safety of new drugs. Physiologically based pharmacokinetic (PBPK) models integrating in vitro-to-in vivo extrapolation have become an essential in silico tool to achieve this goal. In this context, the most important and probably most challenging pharmacokinetic parameter to estimate is the clearance. Recent work on high-throughput PBPK modeling during drug discovery has shown that a good estimate of the unbound intrinsic clearance (CL_int,u,) is the key factor for useful PBPK application. In this work, three different machine learning-based strategies were explored to predict the rat CL_int,u as the input into PBPK. Therefore, in vivo and in vitro data was collected for a total of 2639 proprietary compounds. The strategies were compared to the standard in vitro bottom-up approach. Using the well-stirred liver model to back-calculate in vivo CL_int,u from in vivo rat clearance and then training a machine learning model on this CL_int,u led to more accurate clearance predictions (absolute average fold error (AAFE) 3.1 in temporal cross-validation) than the bottom-up approach (AAFE 3.6-16, depending on the scaling method) and has the advantage that no experimental in vitro data is needed. However, building a machine learning model on the bias between the back-calculated in vivo CL_int,u and the bottom-up scaled in vitro CL_int,u also performed well. For example, using unbound hepatocyte scaling, adding the bias prediction improved the AAFE in the temporal cross-validation from 16 for bottom-up to 2.9 together with the bias prediction. Similarly, the log Pearson r² improved from 0.1 to 0.29. Although it would still require in vitro measurement of CL_int,u., using unbound scaling for the bottom-up approach, the need for correction of the f_u,inc by f_u,p data is circumvented. While the above-described ML models were built on all data points available per approach, it is discussed that evaluation comparison across all approaches could only be performed on a subset because ca. 75% of the molecules had missing or unquantifiable measurements of the fraction unbound in plasma or in vitro unbound intrinsic clearance, or they dropped out due to the blood-flow limitation assumed by the well-stirred model. Advantageously, by predicting CL_int,u as the input into PBPK, existing workflows can be reused and the prediction of the in vivo clearance and other PK parameters can be improved.

Proteins are essential for life, as they participate in all vital processes in the body. In the past decade, delivery of active proteins to specific cells and organs has attracted increasing interest. However, most proteins cannot enter the cytoplasm due to the cell membrane acting as a natural barrier. To overcome this challenge, various proteins have been engineered to acquire cell-penetrating capacity by mimicking or modifying natural shuttling proteins. In this review, we provide an overview of the different types of engineered cell-penetrating proteins such as cell-penetrating peptides, supercharged proteins, receptor-binding proteins, and bacterial toxins. We also discuss some strategies for improving endosomal escape such as pore formation, the proton sponge effect, and hijacking intracellular trafficking pathways. Finally, we introduce some novel methods and technologies for designing and detecting engineered cell-penetrating proteins.

Does the performance of an amorphous solid dispersion rely on having 100% amorphous content? What specifications are appropriate for crystalline content within an amorphous solid dispersion (ASD) drug product? In this Perspective, the origin and significance of crystallinity within amorphous solid dispersions will be considered. Crystallinity can be found within an ASD from one of two pathways: (1) incomplete amorphization, or (2) crystal creation (nucleation and crystal growth). While nucleation and crystal growth is the more commonly considered pathway, where crystals originate as a physical stability failure upon accelerated or prolonged storage, manufacturing-based origins of crystallinity are possible as well. Detecting trace levels of crystallinity is a significant analytical challenge, and orthogonal methods should be employed to develop a holistic assessment of sample properties. Probing the impact of crystallinity on release performance which may translate to meaningful clinical significance is inherently challenging, requiring optimization of dissolution test variables to address the complexity of ASD formulations, in terms of drug physicochemical properties (e.g., crystallization tendency), level of crystallinity, crystal reference material selection, and formulation characteristics. The complexity of risk presented by crystallinity to product performance will be illuminated through several case studies, highlighting that a one-size-fits-all approach cannot be used to set specification limits, as the risk of crystallinity can vary widely based on a multitude of factors. Risk assessment considerations surrounding drug physicochemical properties, formulation fundamentals, physical stability, dissolution, and crystal micromeritic properties will be discussed.

mRNA vaccines encoding a single spike protein effectively prevent severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection. However, the emergence of SARS-CoV-2 variants leads to a wide range of immune evasion. Herein, a unique trivalent mRNA vaccine based on ancestral SARS-CoV-2, Delta, and Omicron variant spike receptor-binding domain (RBD) mRNAs was developed to tackle the immune evasion of the variants. First, three RBD mRNAs of SARS-CoV-2, Delta, and Omicron were coencapsulated into lipid nanoparticles (LNPs) by using microfluidic technology. After that, the physicochemical properties and time-dependent storage stability of the trivalent mRNA vaccine nanoformulation were tested by using dynamic light scattering (DLS). In vitro, the trivalent mRNA vaccine exhibited better lysosomal escape ability, transfection efficiency, and biocompatibility than did the commercial transfection reagent Lipo3000. In addition, Western blot analyses confirmed that the three RBD proteins can be detected in cells transfected with the trivalent mRNA vaccine. Furthermore, ex vivo imaging analysis indicated that the livers of BALB/c mice had the strongest protein expression levels after intramuscular (IM) injection. Using a prime-boost strategy, this trivalent vaccine elicited robust humoral and T-cell immune responses in both the high-dose and low-dose groups and showed no toxicity in BALB/c mice. Three specific IgG antibodies in the high-dose group against SARS-CoV-2, Delta, and Omicron variants approached ∼1/1,833,333, ∼1/1,866,667, and ∼1/925,000, respectively. Taken together, two doses of inoculation with the trivalent mRNA vaccine may provide broad and effective immunization responses against SARS-CoV-2 and variants.

Cardiovascular diseases (CVDs) refer to a collection of conditions characterized by abnormalities in the cardiovascular system. They are a global problem and one of the leading causes of mortality and disability. Nanotheranostics implies to the combination of diagnostic and therapeutic capabilities inside a single nanoscale platform that has allowed for significant advancement in cardiovascular diagnosis and therapy. These advancements are being developed to improve imaging capabilities, introduce personalized therapies, and boost cardiovascular disease patient treatment outcomes. Significant progress has been achieved in the integration of imaging and therapeutic capabilities within nanocarriers. In the case of cardiovascular disease, nanoparticles provide targeted delivery of therapeutics, genetic material, photothermal, and imaging agents. Directing and monitoring the movement of these therapeutic nanoparticles may be done with pinpoint accuracy by using imaging modalities such as cardiovascular magnetic resonance (CMR), computed tomography (CT), positron emission tomography (PET), photoacoustic/ultrasound, and fluorescence imaging. Recently, there has been an increasing demand of noninvasive for multimodal nanotheranostic platforms. In these platforms, various imaging technologies such as optical and magnetic resonance are integrated into a single nanoparticle. This platform helps in acquiring more accurate descriptions of cardiovascular diseases and provides clues for accurate diagnosis. Advances in surface functionalization methods have strengthened the potential application of nanotheranostics in cardiovascular diagnosis and therapy. In this Review, we have covered the potential impact of nanomedicine on CVDs. Additionally, we have discussed the recently developed various nanoparticles for CVDs imaging. Moreover, advancements in the CMR, CT, PET, ultrasound, and photoacoustic imaging for the CVDs have been discussed. We have limited our discussion to nanomaterials based clinical trials for CVDs and their patents.

One of the solubilization of poorly water-soluble drugs is the use of cyclodextrin (CD)-based inclusion complexes. On the other hand, few studies have investigated how CD functions on the solubility of drugs in the presence of multiple drugs that interact with each other. In this study, we used indomethacin (IND) and diclofenac (DIC) as acidic drugs, famotidine (FAM) and cimetidine (CIM) as basic drugs, and imidazole (IMZ), histidine (HIS), and arginine (ARG) as compounds structurally similar to basic drugs. We attempted to clarify the effect of β-CD on the solubility change of each drug in the presence of multiple drugs. IND and DIC formed a eutectic mixture in the presence of CIM, IMZ, and ARG, which greatly increased the intrinsic solubility of the drugs as well as their affinity for β-CD. Furthermore, the addition of high concentrations of β-CD to the DIC–FAM combination, which causes a decrease in solubility due to the interaction, improved the solubility of FAM, which was decreased in the presence of DIC. These results indicate that β-CD synergistically improves the solubility of drugs in drug–drug combinations, where the solubility is improved, whereas it effectively improves the dissolution rate of drugs in situations where the solubility is reduced by drug–drug interactions, such as FAM–DIC. This indicates that β-CD can be used to improve the physicochemical properties of drugs, even when they are administered in combination with drugs that interact with each other.

Grid-based systematic search methods are used to investigate molecule–molecule, molecule–surface, and surface–surface contributions to interparticle interactions in order to identify the crystal faces that most strongly affect particle behavior during powder blend formulation and delivery processes. The model system comprises terbutaline sulfate (TBS) as an active pharmaceutical ingredient (API) and α-form lactose monohydrate (LMH). A combination of systematic molecular modeling and X-ray computed tomography (XCT) is used to determine not only the adhesive and cohesive interparticle energies but, also the agglomeration behavior during manufacturing and de-agglomeration behavior during delivery after inhalation. This is achieved through a detailed examination of the balance between the adhesive and cohesive energies with the XCT results confirming the blend segregation tendencies, through the particle–particle de-agglomeration process. The results reveal that the cohesive interaction energies of TBS–TBS are higher than the adhesive energies between TBS and LMH, but that the cohesive energies of LMH–LMH are the smallest between molecule and molecule, molecule and surface, and surface and surface. This shows how systematic grid-search molecular modeling along with XCT can guide the digital formulation design of inhalation powders in order to achieve optimum aerosolization and efficacy for inhaled medicines. This will lead to faster pharmaceutical design with less variability, higher quality, and enhanced performance.