Molecular Pharmaceutics最新文献

Inhibition of autophagy increases the sensitivity of tumor cells to radiotherapy and chemotherapy and improves the therapeutic effect on tumors. Recently, photodynamic therapy (PDT) combined with chemotherapy has been proven to further improve the efficiency of cancer treatment. As such, combining autophagy inhibition with PDT and chemotherapy may represent a potentially effective new strategy for cancer treatment. However, currently widely studied autophagy inhibitors inevitably produce various toxic side effects due to their inherent pharmacological activity. To overcome this constraint, in this study, we designed an ideal multifunctional upconversion nanoplatform, UCNP-Ce6-EPI@mPPA + NIR (MUCEN). Control, UCNP-EPI@mPPA (MUE), UCNP-EPI@mPPA + NIR (MUEN), Ce6-EPI@mPPA (MCE), Ce6-EPI@mPPA + NIR (MCEN), and UCNP-Ce6-EPI@mPPA (MUCE) groups were set up separately as controls. Based on a combination of autophagy inhibition and PDT, the average particle size of MUCEN was 197 nm, which can simultaneously achieve the double encapsulation of chlorine e6 (Ce6) and epirubicin (EPI). In vitro tests revealed that MUCE was efficiently endocytosed by 4T1 cells under near-infrared light irradiation. Further, in vivo tests revealed that MUCE dramatically inhibited tumor growth. Immunohistochemistry results indicated that MUCE efficiently increased the expression of autophagy inhibitors p62 and LC3 in tumor tissues. The synergistic effect of autophagy inhibition and PDT with MUCE exhibited superior tumor suppression, providing an innovative approach to cancer treatment.

Near UV and visible light photodegradation can target therapeutic proteins during manufacturing and storage. While the underlying photodegradation pathways are frequently not well-understood, one important aspect of consideration is the formulation, specifically the formulation buffer. Citrate is a common buffer for biopharmaceutical formulations, which can complex with transition metals, such as Fe(III). In an aqueous solution, the exposure of such complexes to light leads to the formation of the carbon dioxide radical anion (^•CO₂^-), a powerful reductant. However, few studies have characterized such processes in solid formulations. Here, we show that solid citrate formulations containing Fe(III) lead to the photochemical formation of ^•CO₂^-, identified through DMPO spin trapping and HPLC-MS/MS analysis. Factors such as buffers, the availability of oxygen, excipients, and manufacturing processes of solid formulations were evaluated for their effect on the formation of ^•CO₂^- and other radicals such as ^•OH.

Occult nodal spread and metastatic disease require longstanding imaging and biochemical assessments for thyroid cancer, a disease that has a propensity for diffuse, small-volume disease. We have developed a ⁶⁴Cu-labeled platelet-derived growth factor receptor α (PDGFRA) antibody for immuno-PET of PDGFRA in metastatic papillary thyroid cancer (PTC). The present work describes the discovery of small cyclic PDGFRA-targeting peptides, their binding features, and radiolabeling with positron emitter gallium-68 (⁶⁸Ga) for in vitro and in vivo characterization in thyroid cancer models. Phage-display technology with two separate libraries and seven different cell lines was used through three rounds of biopanning as well as flow cytometry and comparative analysis with recombinant protein to select specific peptide sequences. Phenotypic binding analysis was completed by using phosphorylation and cell migration assays. In vitro protein binding was analyzed with thermophoresis and flow cytometry using the fluorescent-labeled PDGFRA peptide. Peptide candidates were modified with the NOTA chelator for radiolabeling with ⁶⁸Ga. In vitro cell uptake was studied in various thyroid cancer cell lines. In vivo studies of ⁶⁸Ga-labeled peptides included metabolic stability and PET imaging. From the original library (10¹³ compounds), five different peptide groups were identified based on biopanning experiments with and without the α subunit of PDGFR, leading to ∼50 peptides. Subsequent phenotypic screening revealed two core peptide sequences (CP16 and CP18) that demonstrated significant changes in the level of PDGFRA phosphorylation and cell migration. Alanine scan sublibraries were created from these two lead peptide sequences, and peptides were radiolabeled using ⁶⁸Ga-GaCl₃ at pH 4.5, resulting in RCP > 95% within 34-40 min, including SPE purification. Cyclic peptide CP18.5 showed the strongest effects on cell migration, flow cytometry, and binding by visual interference color assay. ⁶⁸Ga-labeled PDGFRA-targeting peptides showed elevated cell and tumor uptake in models of thyroid cancer, with ⁶⁸Ga-NOTA-CP18.5 being the lead candidate. However, metabolic stability in vivo was compromised for ⁶⁸Ga-NOTA-CP18.5 vs ⁶⁸Ga-NOTA-CP18 but without impacting tumor uptake or clearance profiles. First-generation radiolabeled cyclic peptides have been developed as novel radiotracers, particularly ⁶⁸Ga-NOTA-CP18.5, for the molecular imaging of PDGFRA in thyroid cancer.

The increasing focus on patient centricity in the pharmaceutical industry over the past decade and the changing healthcare landscape, driven by factors such as increased access to information, social media, and evolving patient demands, has necessitated a shift toward greater connectivity and understanding of patients' unique treatment needs. One pharmaceutical technology that has supported these efforts is long acting injectables (LAIs), which lower the administration frequency for the patient's provided convenience, better compliance, and hence better therapeutical treatment for the patients. Furthermore, patients with conditions like the human immunodeficiency virus and schizophrenia have positively expressed the desire for less frequent dosing, such as that obtained through LAI formulations. In this work, a comprehensive analysis of marketed LAIs across therapeutic classes and technologies is conducted. The analysis demonstrated an increasing number of new LAIs being brought to the market, recently most as aqueous suspensions and one as a solution, but many other technology platforms were applied as well, in particular, polymeric microspheres and in situ forming gels. The analysis across the technologies provided an insight into to the physicochemical properties the compounds had per technology class as well as knowledge of the excipients typically used within the individual formulation technology. The principle behind the formulation technologies was discussed with respect to the release mechanism, manufacturing approaches, and the possibility of defining predictive in vitro release methods to obtain in vitro in vivo correlations with an industrial angle. The gaps in the field are still numerous, including better systematic formulation and manufacturing investigations to get a better understanding of potential innovations, but also development of new polymers could facilitate the development of additional compounds. The biggest and most important gaps, however, seem to be the development of predictive in vitro dissolution methods utilizing pharmacopoeia described equipment to enable their use for product development and later in the product cycle for quality-based purposes.

Reversible self-association (RSA) of therapeutic proteins presents major challenges in the development of high-concentration formulations, especially those intended for subcutaneous administration. Understanding self-association mechanisms is therefore critical to the design and selection of candidates with acceptable developability to advance to clinical trials. The combination of experiments and in silico modeling presents a powerful tool to elucidate the interface of self-association. RSA of monoclonal antibodies has been studied extensively under different solution conditions and have been shown to involve interactions for both the antigen-binding fragment and the crystallizable fragment. Novel modalities such as bispecific antibodies, antigen-binding fragments, single-chain-variable fragments, and diabodies constitute a fast-growing class of antibody-based therapeutics that have unique physiochemical properties compared to monoclonal antibodies. In this study, the RSA interface of a diabody-interleukin 22 fusion protein (FP-1) was studied using hydrogen-deuterium exchange coupled with mass spectrometry (HDX-MS) in combination with in silico modeling. Taken together, the results show that a complex solution behavior underlies the self-association of FP-1 and that the interface thereof can be attributed to a specific segment in the variable light chain of the diabody. These findings also demonstrate that the combination of HDX-MS with in silico modeling is a powerful tool to guide the design and candidate selection of novel biotherapeutic modalities.

Ionizable lipid-containing lipid nanoparticles (LNPs) have enabled the delivery of RNA for a range of therapeutic applications. In order to optimize safe, targeted, and effective LNP-based RNA delivery platforms, an understanding of the role of composition and pH in their structural properties and self-assembly is crucial, yet there have been few computational studies of such phenomena. Here we present a coarse-grained model of ionizable lipid and mRNA-containing LNPs. Our model allows access to the large length- and time-scales necessary for LNP self-assembly and is mapped and parametrized with reference to all-atom structures and simulations of the corresponding components at compositions typical of LNPs used for mRNA delivery. Our simulations reveal insights into the dynamics of self-assembly of such mRNA-encapsulating LNPs, as well as the subsequent pH change-driven LNP morphology and release of mRNA.

Folate uptake is largely mediated by folate receptor (FR)β, encoded by FOLR2 gene, in myeloid immune cells such as granulocytes, monocytes, and especially in macrophages that constitute the reticuloendothelial system (RES) and infiltrate the tumor microenvironment. Since the myeloid immune compartment dynamically changes during tumorigenesis, it is critical to assess the infiltration status of the tumors by FRβ-expressing myeloid cells to better define the targeting efficacy of folate-functionalized drug delivery systems. On the other hand, clearance by RES is a major limitation for the targeting efficacy of nanoparticles decorated with folate. Therefore, the aims of this study are (i) to determine the amount and subtypes of FRβ⁺ myeloid cells infiltrating the tumors at different stages, (ii) to compare the amount and subtype of FRβ⁺ myeloid cells in distinct organs of tumor-bearing and healthy animals, (iii) to test if the cancer-targeting efficacy and biodistribution of a prototypic folate-functionalized nanoparticle associates with the density of FRβ⁺ myeloid cells. Here, we report that myeloid cell infiltration was enhanced and FRβ was upregulated at distinct stages of tumorigenesis in a mouse breast cancer model. The CD206⁺ subset of macrophages highly expressed FRβ, prominently both in tumor-bearing and healthy mice. In tumor-bearing mice, the amount of all myeloid cells, but particularly granulocytes, was remarkably increased in the tumor, liver, lungs, spleen, kidneys, lymph nodes, peritoneal cavity, bone marrow, heart, and brain. Compared with macrophages, the level of FRβ was moderate in granulocytes and monocytes. The density of FRβ⁺ immune cells in the tumor microenvironment was not directly associated with the tumor-targeting efficacy of the folate-functionalized cyclodextrin nanoparticles. The lung was determined as a preferential site of accumulation for folate-functionalized nanoparticles, wherein FRβ⁺CD206⁺ macrophages significantly engulfed cyclodextrin nanoparticles. In conclusion, our results demonstrate that the tumor formation augments the FR levels and alters the infiltration and distribution of myeloid immune cells in all organs which should be considered as a major factor influencing the targeting efficacy of nanoparticles for drug delivery.

Intracellular delivery of biological cargos, which would yield new research tools and novel therapeutics, remains an active area of research. A convenient and potentially general approach involves the conjugation of a cell-penetrating peptide to a cargo of interest. However, linear CPPs lack sufficient cytosolic entry efficiency and metabolic stability, while previous backbone cyclized CPPs have several drawbacks including the necessity for chemical synthesis and posttranslational conjugation to peptide/protein cargos and epimerization during cyclization. We report here a new class of bismuth cyclized CPPs with excellent cytosolic entry efficiencies, proteolytic stability, and potential compatibility with genetic encoding and recombinant production.

Transdermal microneedles have demonstrated promising potential as an alternative to typical drug administration routes for the treatment of various diseases. As microneedles offer lower administration burden with enhanced patient adherence and reduced ecological footprint, there is a need for further exploitation of microneedle devices. One of the main objectives of this work was to initially develop an innovative biobased photocurable resin with high biobased carbon content comprising isobornyl acrylate (IBA) and pentaerythritol tetraacrylate blends (50:50 wt/wt). The optimization of the printing and curing process resulted in μNe3dle arrays with durable mechanical properties and piercing capacity. Another objective of the work was to employ the 3D printed hollow μNe3dles for the treatment of osteoporosis in vivo. The 3D printed μNe3dle arrays were used to administer denosumab (Dmab), a monoclonal antibody, to osteoporotic mice, and the serum concentrations of critical bone minerals were monitored for six months to assess recovery. It was found that the Dmab administered by the 3D printed μNe3dles showed fast in vitro rates and induced an enhanced therapeutic effect in restoring bone-related minerals compared to subcutaneous injections. The findings of this study introduce a novel green approach with a low ecological footprint for 3D printing of biobased μNe3dles, which can be tailored to improve clinical outcomes and patient compliance for chronic diseases.

We present a novel computational approach for predicting human pharmacokinetics (PK) that addresses the challenges of early stage drug design. Our study introduces and describes a large-scale data set of 11 clinical PK end points, encompassing over 2700 unique chemical structures to train machine learning models. To that end multiple advanced training strategies are compared, including the integration of in vitro data and a novel self-supervised pretraining task. In addition to the predictions, our final model provides meaningful epistemic uncertainties for every data point. This allows us to successfully identify regions of exceptional predictive performance, with an absolute average fold error (AAFE/geometric mean fold error) of less than 2.5 across multiple end points. Together, these advancements represent a significant leap toward actionable PK predictions, which can be utilized early on in the drug design process to expedite development and reduce reliance on nonclinical studies.