Pharmaceutical Research最新文献

Purpose: The removal of the residual solvent dichloromethane from biodegradable poly(D,L-lactic-co-glycolic acid) (PLGA) microparticles was investigated by aqueous or alcoholic wet extraction or vacuum-drying.

Methods: Microparticles were prepared by the O/W solvent extraction/evaporation method. The solidified microparticles were separated by filtration and the effect of subsequent drying and wet extraction methods were investigated. The residual solvent content was analysed with gas chromatography (organic solvents) and Karl Fischer titration (water). The effect of extraction conditions on microparticle aggregation, surface morphology and encapsulation of the drugs dexamethasone and risperidone was investigated.

Results: Residual dichloromethane was reduced to 2.43% (w/w) (20 °C) or 0.03% (w/w) (35 °C) by aqueous wet extraction. With vacuum-drying, residual dichloromethane only decreased from about 5% (w/w) to 4.34% (w/w) (20 °C) or 3.20% (w/w) (35 °C) due to the lack of the plasticizing effect of water. Redispersion of filtered, wet microparticles in alcoholic media significantly improved the extraction due to an increased PLGA plasticization. The potential of different extractants was explained with the Gordon-Taylor equation and Hansen solubility parameters. Extraction in methanol: or ethanol:water mixtures reduced residual dichloromethane from 4 - 7% (w/w) to 0.5 - 2.3% (w/w) within 1 h and 0.08 - 0.18% (w/w) within 6 h. Higher alcohol contents and higher temperature resulted in aggregation of microparticles and lower drug loadings.

Conclusion: The final removal of residual dichloromethane was more efficient with alcoholic wet extraction followed by aqueous wet extraction at elevated temperature and vacuum drying of the microparticles.

Objective: Resveratrol-piperazine cocrystals have been obtained by ultrasound (US) and microwave-assisted (MW) techniques, using the solution and slurry-based methods, to study the influence of the synthesis method on the resulting cocrystal properties, and scalability of the processes. The potential of these cocrystals is represented by the unique properties of their components, resveratrol, and piperazine, which could be also used in veterinary practice. Resveratrol has antimicrobial, antiviral and anticarcinogenic properties, while piperazine can be used in the treatment of parasitic infections.

Methods: The influence of ultrasound and microwave-assisted treatment was studied by varying synthesis parameters such as reaction time, temperature, and US or MW power. The main advantage of using these methods is represented by shorter synthesis time compared to conventional methods, resulting in the direct formation of the cocrystals.

Results: All samples were obtained in high purity, above 97%. Cocrystal yield correlated positively with ultrasound reaction time, while temperature was not found to influence the microwave synthesis yield up to 50°C, in the case of solution-based methods. MW and US-assisted solution-based methods lead to yields between 52.9 and 68.1%. In the case of the slurry-based method, a minimum reaction time of 5 min leads to the formation of cocrystals with high purity. The resveratrol-piperazine cocrystal's solubility and in vitro antibacterial activity were also evaluated, showing promising results.

Conclusions: Ultrasound and microwave-assisted techniques offer a viable alternative for synthesizing resveratrol-piperazine cocrystals with short reaction times, high yield, and purity, suitable for scalable resveratrol-piperazine cocrystals.

Purpose: The study aims to leverage the capabilities of Liquid Chromatography-Multiple Reaction Monitoring Mass Spectrometry (LC-MRM), a key technique in quantifying therapeutic proteins in pharmacokinetic studies. The focus is on demonstrating an enrichment method using ProteoMiner beads, which can be integrated with LC-MRM to detect low-abundance biotherapeutics in serum, such as monoclonal antibodies and gene therapy products.

Methods: The ProteoMiner enrichment method was employed and integrated with LC-MRM. The lower limit of quantification of serum drug substance concentrations was compared with that achievable with immuno-enrichment. The method used commercially available reagents, eliminating the need for assay-specific antibodies and reducing potential bias and development time.

Results: The ProteoMiner enrichment method showed comparable performance to immuno-enrichment, meeting traditional assay requirements in terms of precision, accuracy, and specificity.

Conclusions: The ProteoMiner enrichment method, when combined with LC-MRM, offers a reliable and efficient alternative to immuno-enrichment for detecting and quantifying low-abundance biotherapeutics in serum. This approach, which uses commercially available reagents, can eliminate the bias and time associated with the development of assay-specific antibodies. It holds significant potential for accelerating pharmacokinetic analysis in both early and late stages of pharmaceutical development.

Purposes: Size exclusion chromatography (SEC) is widely used to characterize molecular size variants of antibody drugs. However, SEC analysis is hindered by secondary interactions (or nonspecific interactions) between proteins and stationary phase packing, which result in poor column efficiency. Previous studies have reported that chaotropic salt can inhibit these interactions, but the corresponding applications of this aspect are relatively rare. Therefore, this study introduces a novel approach using sodium iodide (NaI) as a mobile-phase component in SEC and investigates the influence of the mobile-phase composition on secondary interactions.

Methods: SEC analysis was performed on one antibody-drug conjugate and four monoclonal antibodies (mAbs) using three different mobile-phase systems (i.e., sodium chloride/L-arginine hydrochloride/NaI mobile phases system) to compare the column efficiency. Subsequently, mAb-1 was used as a model to investigate the effects of these factors on secondary interactions by adjusting the ionic strength (salt concentration) and pH of the NaI mobile-phase system.

Results: NaI exhibits superior column efficiency performance in the SEC analysis of most products. The ionic strength will affect nonideal electrostatic and hydrophobic interaction. An appropriate ionic strength can inhibit electrostatic interactions, while an excessive ionic strength increases hydrophobic interactions. pH primarily influences electrostatic interactions. Determining the appropriate pH necessitates consideration of the isoelectric point of the protein and the pH tolerance of the column.

Conclusions: In SEC analysis, using NaI as the salt component in the mobile phase reduces secondary interactions and improves column efficiency. This approach is advantageous for samples with intense secondary interactions and is a suitable alternative.

Purpose or objective: Drug concentration-response curves (DRCs) are crucial in pharmacology for assessing the drug effects on biological systems. The widely used sigmoid Emax model, which accounts for response saturation, relies heavily on the effective drug concentration ( $E{D}_{50}$ ). This reliance can lead to validation errors and inaccuracies in model fitting. The Emax model cannot generate multiple DRCs, raising concerns about whether the dataset is fully utilized.

Methods: This study formulates an extended Emax (eEmax) model designed to overcome these limitations. The eEmax model generates multiple DRCs from a single dataset by using various estimated ${\alpha }^{\text{'}}\text{s}\in \left(\text{0,100}\right)$ , while keeping $E{D}_{\alpha }$ fixed, rather than estimating an $E{D}_{50}$ value as in the Emax model.

Results: This model effectively captures a broader range of concentration-response behavior, including non-sigmoidal patterns, thus providing greater flexibility and accuracy compared to the Emax model. Validation using various drug-response data and PKPD frameworks demonstrates the eEmax model's improved accuracy and versatility in handling concentration-response data.

Conclusions: The eEmax model provides a robust and flexible method for drug concentration-response analysis, facilitating the generation of multiple DRCs from a single dataset and reducing the possibility of validation errors. This model is particularly valuable for its ease of use and its capability to fully utilize datasets, providing its potential in PKPD modeling and drug discovery.

Purpose: Alzheimer's disease (AD) is associated with brain accumulation of amyloid-beta (Aβ) and neurofibrillary tangle formation, in addition to reduced brain docosahexaenoic acid (DHA) and increased brain iron levels. DHA requires access across the blood-brain barrier (BBB) to enter the brain, and iron has been shown to affect the expression and function of a number of BBB transporters. Therefore, this study aimed to assess the effect of iron on the expression and function of fatty acid binding protein 5 (FABP5) and fatty acid transport protein 1 (FATP1), both which mediate brain endothelial cell trafficking of DHA.

Methods: The mRNA and protein levels of FABP5 and FATP1 in human cerebral microvascular endothelial (hCMEC/D3) cells was assessed by RT-qPCR and Western blot, respectively following ferric ammonium citrate (FAC) treatment (up to 750 µM, 72 h). The function of FABP5 and FATP1 was assessed via uptake and efflux of radiolabelled ³H-oleic acid and ¹⁴C-DHA.

Results: FAC (500 µM, 72 h) had no impact on the expression of FABP5 at the protein and mRNA level in hCMEC/D3 cells, which was associated with a lack of effect on the uptake of ¹⁴C-DHA. FAC led to a 19.7% reduction in FATP1 protein abundance in hCMEC/D3 cells with no impact on mRNA levels, and this was associated with up to a 32.6% reduction in efflux of ¹⁴C-DHA.

Conclusions: These studies demonstrate a role of iron in down-regulating FATP1 protein abundance and function at the BBB, which may have implications on fatty acid access to the brain.

The emergence of antimicrobic-resistant infectious pathogens and the consequent rising in the incidence and prevalence of demises caused by or associated to infections which are not sensitive to drug treatments is one of today's major global health challenges. Antimicrobial resistance (AMR) can bring to therapeutic failure, infection's persistence and risk of serious illness, in particular in vulnerable populations such as the elderly, patients with neoplastic diseases or the immunocompromised. It is assessed that AMR will induce until 10 million deaths per year by 2050, becoming the leading cause of disease-related deaths. The World Health Organisation (WHO) and the United Nations General Assembly urgently call for new measures to combat the phenomenon. Research and development of new antimicrobial agents has decreased due to market failure. However, promising results are coming from new alternative therapeutic strategies such as monoclonal antibodies, microbiome modulators, nanomaterial-based therapeutics, vaccines, and phages. This narrative review aimed to analyse the benefits and weaknesses of alternative therapeutic strategies to antibiotics which treat multidrug-resistant bacterial infections.

Objective: Currently, 90% of clinical drug development fails, where 30% of these failures are due to clinical toxicity. The current extensive animal toxicity studies are not predictive of clinical adverse events (AEs) at clinical doses, while current computation models only consider very few factors with limited success in clinical toxicity prediction. We aimed to address these issues by developing a machine learning (ML) model to directly predict clinical AEs.

Methods: Using a dataset with 759 FDA-approved drugs with known AEs, we first adapted the ConPLex ML model to predict IC $_{50}^{}$ values of these FDA-approved drugs against their on-target and off-target binding among 477 protein targets. Subsequently, we constructed a new ML model to predict clinical AEs using IC $_{50}^{}$ values of 759 drugs' primary on-target and off-target effects along with tissue-specific protein expression profiles.

Results: The adapted ConPLex model predicted drug-target interactions for both on- and off-target effects, as shown by co-localization of the 6 small molecule kinase inhibitors with their respective kinases. The coupled ML models demonstrated good predictive capability of clinical AEs, with accuracy over 75%.

Conclusions: Our approach provides a new insight into the mechanistic understanding of in vivo drug toxicity in relationship with drug on-/off-target interactions. The coupled ML models, once validated with larger datasets, may offer advantages to directly predict clinical AEs using in vitro/ex vivo and preclinical data, which will help to reduce drug development failure due to clinical toxicity.

This paper reviews the accelerated development of pharmaceuticals, exploring past, present, and future perspectives. It provides a historical overview of early strategies used to expedite development, beginning with initiatives from the 1990s. The work of Gardner and Byrn in accelerated development analysis during this era is highlighted. The narrative progresses to the 2000s, discussing the emergence of PK/PD in accelerating pharmaceutical development. The paper further examines case studies in the accelerated development field, including the INDIGO and Chorus programs. It concludes with an analysis of the current state of the field, referencing the NIPTE conference, which focused on the industrial perspective of accelerated development. Additionally, the paper outlines strategies for the rapid development of Solid Lipid Nanoparticle manufacturing and vaccine production.