Rationalizing Counterion Selection for the Development of Lipophilic Salts: A Case Study with Venetoclax.

The use of lipid-based formulations (LBFs) can be hindered by low dose loading due to solubility limitations of candidate drugs in lipid vehicles. Formation of lipophilic salts through pairing these drugs with a lipophilic counterion has been demonstrated as a potential means to enhance dose loading in LBFs. This study investigated the screening of appropriate counterions to form lipophilic salts of the BCS class IV drug venetoclax. The physical properties, lipid solubility, and in vitro performance of the salts were analyzed. This study illustrated the versatility of alkyl sulfates and sulfonates as suitable counterions in lipophilic salt synthesis with up to ∼9-fold higher solubility in medium- and long-chain LBFs when compared to that of the free base form of venetoclax. All salts formulated as LBFs displayed superior in vitro performance when compared to the free base form of the drug due to the higher initial drug loadings in LBFs and increased affinity for colloidal species. Further, in vitro studies confirmed that venetoclax lipophilic salt forms using alkyl chain counterions demonstrated comparable in vitro performance to venetoclax docusate, thus reducing the potential for laxative effects related to docusate administration. High levels of the initial dose loading of venetoclax lipophilic salts were retained in a molecularly dispersed state during dispersion and digestion of the formulation, while also demonstrating increased levels of saturation in biorelevant media. The findings of this study suggest that alkyl chain sulfates and sulfonates can act as a suitable alternative counterion to docusate, facilitating the selection of counterions that can unlock the potential to formulate venetoclax as an LBF.

Exploring career choices of pharmacy graduates over 15 years: A cross-sectional evaluation.

INTRODUCTION: Career opportunities for pharmacists beyond those commonly associated with the degree continue to emerge. A paucity of literature regarding evaluation of pharmacy graduate career paths over extended periods is apparent. Considering international pharmacy workforce capacity pressures, the primary study aim was to evaluate trends in career paths of pharmacy graduates. METHODS: This study utilised a multimethod approach to access graduate career data using publicly accessible information from LinkedIn® profiles and an online survey. The survey was distributed to all pharmacy graduates of a university (2007-2022). Data from both methods was combined, cross-checked, coded and analysed quantitatively using descriptive and inferential statistics. RESULTS: Data from 69.7% of the university's pharmacy graduates was collected. Community pharmacy was the most prevalent employment sector (47.7%), followed by industry (21.5%) and hospital (17.7%). A higher proportion of more recent graduates (≤5 years post-graduation) work in a community or hospital pharmacy role versus those who graduated greater than five years ago (χ2 = 8.44, df = 2, p < 0.05). Post-graduate education was undertaken by 41.3% of graduates. Career satisfaction was high (88.2%) but was lower (χ2 = 11.31, df = 1, p < 0.05) for those in community and hospital (82%) versus other sectors (97.5%). CONCLUSION: This study provides the first analysis of graduate career paths over an extended period, highlighting a novel approach to track pharmacist workforce. While almost two thirds of pharmacy graduates occupy community or hospital roles, a trend of leaving these settings five years post-graduation was evident. Accordingly, this work represents a springboard for additional research to inform future pharmacist workforce planning worldwide.

Public perceptions of pharmacogenomic services in Ireland - Are people with chronic disease more likely to want service availability than those without? A questionnaire study.

Background: As pharmacogenomic services begin to emerge in primary care, the insight of the public is crucial for its integration into clinical practice. Objectives: To establish perceptions of pharmacogenomics (awareness, understanding, openness to availability, perceived benefits and concerns, willingness to pay, and service setting) and investigate if they differ between those with and without chronic disease(s). Methods: An anonymous, online questionnaire generated using Qualtrics® and circulated via social media and posters placed in eight participating community pharmacies was conducted with Irish adults. The questions were designed to consider existing literature on patient perceptions of pharmacogenomics. Descriptive statistics were used to summarize questionnaire responses. Chi-square test was used to compare categorical variables, while independent sample t-test and one-way ANOVA were used to compare the mean values of two (with and without chronic disease) and three groups (multimorbidity (two or more chronic conditions) and polypharmacy (prescribed four or more regular medicines) (MMPP), a single chronic disease, and those without existing medical conditions) respectively Logistic regression was used to evaluate age and gender adjusted associations of chronic disease(s) with responses. A p-value <0.05 was considered statistically significant. Results: A total of 421 responses were received, 30% (n = 120) of whom reported having a chronic disease. Overall, respondents reported low awareness (44%, n = 166) and poor knowledge (55%, n = 212) of pharmacogenomics. After explaining pharmacogenomics to respondents, patients with chronic disease(s) were 2.17 times more likely (p < 0.001) to want pharmacogenomic services availability than those without existing conditions, adjusted for age and gender (driven by preferences of those with MMPP than those with single chronic disease). Respondents demonstrated a high level of interest and noted both the potential benefits and downsides of pharmacogenomic testing. Willingness-to-pay was not associated with having a chronic disease and respondents were more positive about primary care (community pharmacy or general practice) rather than hospital-based pharmacogenomics implementation. Conclusion: The Irish public in general and those with chronic disease in particular are strongly supportive of pharmacogenomic testing, highlighting an unmet need for its incorporation in medicines optimization. These data underline the need for more research on the implementation of community-based pharmacogenomics services for MMPP patients and ubiquitous pharmacogenomics education programs.

The Okur-Chung Neurodevelopmental Syndrome Mutation CK2K198R Leads to a Rewiring of Kinase Specificity.

Okur-Chung Neurodevelopmental Syndrome (OCNDS) is caused by heterozygous mutations to the CSNK2A1 gene, which encodes the alpha subunit of protein kinase CK2. The most frequently occurring mutation is lysine 198 to arginine (K198R). To investigate the impact of this mutation, we first generated a high-resolution phosphorylation motif of CK2WT, including the first characterization of specificity for tyrosine phosphorylation activity. A second high resolution motif representing CK2K198R substrate specificity was also generated. Here we report the impact of the OCNDS associated CK2K198R mutation. Contrary to prior speculation, the mutation does not result in a complete loss of function, but rather shifts the substrate specificity of the kinase. Broadly speaking the mutation leads to 1) a decreased preference for acidic residues in the +1 position, 2) a decreased preference for threonine phosphorylation, 3) an increased preference for tyrosine phosphorylation, and 4) an alteration of the tyrosine phosphorylation specificity motif. To further investigate the result of this mutation we have developed a probability-based scoring method, allowing us to predict shifts in phosphorylation in the K198R mutant relative to the wild type kinase. As an initial step we have applied the methodology to the set of axonally localized ion channels in an effort to uncover potential alterations of the phosphoproteome associated with the OCNDS disease condition.

Pharmacogenetic interventions to improve outcomes in patients with multimorbidity or prescribed polypharmacy: a systematic review.

Conventional medicines optimisation interventions in people with multimorbidity and polypharmacy are complex and yet limited; a more holistic and integrated approach to healthcare delivery is required. Pharmacogenetics has potential as a component of medicines optimisation. Studies involving multi-medicine pharmacogenetics in adults with multimorbidity or polypharmacy, reporting on outcomes derived from relevant core outcome sets, were included in this systematic review. Narrative synthesis was undertaken to summarise the data; meta-analysis was inappropriate due to study heterogeneity. Fifteen studies of diverse design and variable quality were included. A small, randomised study involving pharmacist-led medicines optimisation, including pharmacogenetics, suggests this approach could have significant benefits for patients and health systems. However, due to study design heterogeneity and the quality of the included studies, it is difficult to draw generalisable conclusions. Further pragmatic, robust pharmacogenetics studies in diverse, real-world patient populations, are required to establish the benefit of multi-medicine pharmacogenetic screening on patient outcomes.

Machine learning methods for prediction of food effects on bioavailability: A comparison of support vector machines and artificial neural networks.

Despite countless advances in recent decades across various in vitro, in vivo and in silico tools, anticipation of whether a drug will show a human food effect (FE) remains challenging. One means to predict potential FE involves probing any dependence between FE and drug properties. Accordingly, this study explored the potential for two machine learning (ML) algorithms to predict likely FE. Using a collated database of drugs licensed from 2016-2020, drugs were classified into three groups; positive, negative or no FE. Greater than 250 drug properties were predicted for each drug which were used to train predictive models using Support Vector Machine (SVM) and Artificial Neural Network (ANN) algorithms. When compared, ANN outperformed SVM for FE classification upon training (82%, 72%) and testing (72%, 69%). Both models demonstrated higher FE prediction accuracy than the Biopharmaceutics Classification System (BCS) (46%). This exploratory work provided new insights into the connection between FE and drug properties as the Octanol Water Partition Coefficient (S+logP), Number of Hydrogen Bond Donors (HBD), Topological Polar Surface Area (T_PSA) and Dose (mg) were all significant for prediction. Overall, this study demonstrated the utility of ML to facilitate early anticipation of likely FE in pre-clinical development using four well-known drug properties.

Best practices in current models mimicking drug permeability in the gastrointestinal tract - An UNGAP review.

The absorption of orally administered drug products is a complex, dynamic process, dependant on a range of biopharmaceutical properties; notably the aqueous solubility of a molecule, stability within the gastrointestinal tract (GIT) and permeability. From a regulatory perspective, the concept of high intestinal permeability is intrinsically linked to the fraction of the oral dose absorbed. The relationship between permeability and the extent of absorption means that experimental models of permeability have regularly been used as a surrogate measure to estimate the fraction absorbed. Accurate assessment of a molecule's intestinal permeability is of critical importance during the pharmaceutical development process of oral drug products, and the current review provides a critique of in vivo, in vitro and ex vivo approaches. The usefulness of in silico models to predict drug permeability is also discussed and an overview of solvent systems used in permeability assessments is provided. Studies of drug absorption in humans are an indirect indicator of intestinal permeability, but both in vitro and ex vivo tools provide initial screening approaches and are important tools for assessment of permeability in drug development. Continued refinement of the accuracy of in silico approaches and their validation with human in vivo data will facilitate more efficient characterisation of permeability earlier in the drug development process and will provide useful inputs for integrated, end-to-end absorption modelling.

Artificial Neural Networks to Predict the Apparent Degree of Supersaturation in Supersaturated Lipid-Based Formulations: A Pilot Study.

In response to the increasing application of machine learning (ML) across many facets of pharmaceutical development, this pilot study investigated if ML, using artificial neural networks (ANNs), could predict the apparent degree of supersaturation (aDS) from two supersaturated LBFs (sLBFs). Accuracy was compared to partial least squares (PLS) regression models. Equilibrium solubility in Capmul MCM and Maisine CC was obtained for 21 poorly water-soluble drugs at ambient temperature and 60 °C to calculate the aDS ratio. These aDS ratios and drug descriptors were used to train the ML models. When compared, the ANNs outperformed PLS for both sLBFCapmulMC (r2 0.90 vs. 0.56) and sLBFMaisineLC (r2 0.83 vs. 0.62), displaying smaller root mean square errors (RMSEs) and residuals upon training and testing. Across all the models, the descriptors involving reactivity and electron density were most important for prediction. This pilot study showed that ML can be employed to predict the propensity for supersaturation in LBFs, but even larger datasets need to be evaluated to draw final conclusions.

Combining species specific in vitro & in silico models to predict in vivo food effect in a preclinical stage - case study of Venetoclax.

The pig has been increasingly used as a reliable preclinical model for assessing and predicting the in vivo bioavailability of different formulation strategies. Nevertheless, differences in the composition between porcine and human intestinal fluids, may impact on the solubility and dissolution behaviour of drugs, in particular BCS II/IV drugs. Recently, a porcine fasted simulated intestinal fluid (FaSSIFp) was developed to mimic the composition in the lumen of landrace pigs under fasted state conditions. In this work, we present the utilization of FaSSIFp to compare solubility against human FaSSIF & FeSSIF and further combine species specific in vitro testing with in silico predictive modelling. Venetoclax was chosen as a model drug, representing a BCS class IV drug, with a reported clinically significant positive food effect, where bioavailability is increased up to approximately five-fold when administered with a high-fat meal. Biorelevant species specific in vitro testing was a promising tool for integrating in vitro data into in silico models, using FaSSIFp resulted in reliable predictions of the plasma concentration profile in fasted pigs, based on a porcine physiologically based absorption model. The porcine physiologically based absorption model was used to prospectively simulate the impact of food on the bioavailability of venetoclax. The use of luminal solubility estimates in combination with dissolution data for venetoclax, measured in species specific simulated fluids, correctly predict the observed pig plasma concentration profile and food effect. Overall, integrating species specific in vitro - in silico models led to accurate prediction of in vivo absorption of venetoclax in a preclinical stage, which can support guidance in early decisions of drug product development. In addition, the study further demonstrated the utility of the pig model to predict the food effects of venetoclax in humans.

Exploring porcine gastric and intestinal fluids using microscopic and solubility estimates: Impact of placebo self-emulsifying drug delivery system administration to inform bio-predictive in vitro tools.

Validation and characterisation of in vitro and pre-clinical animal models to support bio-enabling formulation development is of paramount importance. In this work, post-mortem gastric and small intestinal fluids were collected in the fasted, fed state and at five sample-points post administration of a placebo Self-Emulsifying Drug Delivery System (SEDDS) in the fasted state to pigs. Cryo-TEM and Negative Stain-TEM were used for ultrastructure characterisation. Ex vivo solubility of fenofibrate was determined in the fasted-state, fed-state and post-SEDDS administration. Highest observed ex vivo drug solubility in intestinal fluids after SEDDS administration was used for optimising the biorelevant in vitro conditions to determine maximum solubility. Under microscopic evaluation, fasted, fed and SEDDS fluids resulted in different colloidal structures. Drug solubility appeared highest 1 hour post SEDDS administration, corresponding with presence of SEDDS lipid droplets. A 1:200 dispersion of SEDDS in biorelevant media matched the highest observed ex vivo solubility upon SEDDS administration. Overall, impacts of this study include increasing evidence for the pig preclinical model to mimic drug solubility in humans, observations that SEDDS administration may poorly mimic colloidal structures observed under fed state, while microscopic and solubility porcine assessments provided a framework for increasingly bio-predictive in vitro tools.

Applying Computational Predictions of Biorelevant Solubility Ratio Upon Self-Emulsifying Lipid-Based Formulations Dispersion to Predict Dose Number.

Computational approaches are increasingly utilised in development of bio-enabling formulations, including self-emulsifying drug delivery systems (SEDDS), facilitating early indicators of success. This study investigated if in silico predictions of drug solubility gain i.e. solubility ratios (SR), after dispersion of a SEDDS in biorelevant media could be predicted from drug properties. Apparent solubility upon dispersion of two SEDDS in FaSSIF was measured for 30 structurally diverse poorly water soluble drugs. Increased drug solubility upon SEDDS dispersion was observed in all cases, with higher SRs observed for cationic and neutral versus anionic drugs at pH 6.5. Molecular descriptors and solid-state properties were used as inputs during partial least squares (PLS) modelling resulting in predictive models for SRMC (r2 = 0.81) and SRLC (r2 = 0.77). Multiple linear regression (MLR) facilitated generation of simplified SR equations with high predictivity (SRMC r2 = 0.74; SRLC r2 = 0.69), requiring only three drug properties; partition coefficient at pH 6.5 (logD6.5), melting point (Tm) and aromatic bonds as fraction of total bonds (F-AromB). Through using the equations to inform developability classification system (DCS) classes for drugs that have already been licensed as lipid-based formulations, merits for development with SEDDS was predicted for 2/3 drugs.

Pre-Clinical Evaluation of a Modified Cyclodextrin-Based Nanoparticle for Intestinal Delivery of Liraglutide.

New therapeutic approaches have been developed during recent years for the management of diabetic patients, with glucagon-like peptides analogues (GLP-1 analogues) emerging as one of the most useful therapies. However, as with human insulin analogues, translation of GLP-1 analogues into oral pharmaceutical products has been limited due to reduced oral bioavailability. Nanoparticle (NP) formulations have been investigated due to their potential to protect the drug cargo and enhance bioavailability. This study describes the pre-clinical development of a cyclodextrin-based NP formulation containing the GLP-1 analogue liraglutide for intestinal administration. A cationic amphiphilic cyclodextrin (click propyl-amine cyclodextrin (CD)) was selected as the primary complexing agent for the peptide. The resulting NPs presented an average size of 101 ± 8 nm, low polydispersity index (0.240), a negative zeta potential (-35 ± 7 mV), complete association efficiency and peptide loading of 5.0%. The optimized prototype exhibited colloidal stability in intestinal-biorelevant media up to 4 h, protecting the entrapped liraglutide from degradation by proteolytic enzymes. Intestinal administration in rats revealed effective protection and delivery of liraglutide, with a similar pharmacological response in blood glucose levels relative to subcutaneous administration of free solution. These results demonstrate the potential of the CD based formulation for further development.

A Retrospective Biopharmaceutical Analysis of >800 Approved Oral Drug Products: Are Drug Properties of Solid Dispersions and Lipid-Based Formulations Distinctive?

Increasing numbers of poorly water soluble drugs in development has intensified need for bio-enabling formulations including Lipid-Based Formulations (LBF) and Solid Dispersions (SD). Resultantly, a data-driven approach is required to increase formulation development efficiency. This review provides a retrospective analysis of molecular and biopharmaceutical properties of drugs commercialised as LBFs or SDs. A comprehensive stepwise statistical analysis of LBF and SD drug properties was conducted and compared to drugs not commercialised via either technology (Others), aiming to identify key predictors of successful formulation development. This review demonstrates LBF and SD drugs differ significantly in molecular weight, polar surface area, rotatable bonds and hydrogen bond acceptor count. Meanwhile, LBF and SD drugs display significantly different aqueous solubility, lipophilicity, size, molecular flexibility, hydrogen bonding capacity and rule-of-5 violations versus Others. LBF and SDs were 3 and 5 times more likely to display >1 rule-of-5 violation versus Others, over 55% of LBF drugs exceeded the reported melting point guide of <150 °C, while 24% of SD drugs contained >10 Hydrogen Bond Acceptors. Overall, by focusing on successfully commercialised drugs, this review provides improved understanding of links between drug properties and successful SD/LBF approaches, providing a framework for guiding pharmaceutical development on formulation approaches.

Toward the establishment of a standardized pre-clinical porcine model to predict food effects - Case studies on fenofibrate and paracetamol.

A preclinical porcine model that reliably predicts human food effect of fenofibrate was developed. Fenofibrate was administered to pigs as model compound with a positive food effect. Two different types of fed conditions were explored: a FDA style breakfast and a standard pig pellet feed. In order to assess if complete stomach emptying had been achieved under the employed fasting protocol, the amount of gastric and intestinal content was evaluated post-mortem. In addition, the protocol was designed to evaluate gastric emptying in the pre- and postprandial state using paracetamol as a marker. The study confirmed that micronized fenofibrate displayed a positive food effect with a similar fold difference to humans in FDA style fed state. Post-mortem assessment of stomach and intestinal content confirmed significantly lower content in the fasted compared to the pig pellet fed state. In the case of paracetamol, a delayed gastric emptying in the fed state was not observed, which may suggest that the Magenstrasse phenomena reported in humans, may also occur in landrace pigs. The study demonstrated the utility of a food effect protocol in landrace pigs as a pre-clinical approach to predict human food effects and provided new insights into gastric emptying in pigs.

The pig as a preclinical model for predicting oral bioavailability and in vivo performance of pharmaceutical oral dosage forms: a PEARRL review.

OBJECTIVES: In pharmaceutical drug development, preclinical tests in animal models are essential to demonstrate whether the new drug is orally bioavailable and to gain a first insight into in vivo pharmacokinetic parameters that can subsequently be used to predict human values. Despite significant advances in the development of bio-predictive in vitro models and increasing ethical expectations for reducing the number of animals used for research purposes, there is still a need for appropriately selected pre-clinical in vivo testing to provide guidance on the decision to progress to testing in humans. The selection of the appropriate animal models is essential both to maximise the learning that can be obtained from such experiments and to avoid unnecessary testing in a range of species. KEY FINDINGS: The present review, provides an insight into the suitability of the pig model for predicting oral bioavailability in humans, by comparing the conditions in the GIT. It also contains a comparison between the bioavailability of compounds dosed to both humans and pigs, to provide an insight into the relative correlation and examples on why a lack of correlation may be observed. SUMMARY: While there is a general trend towards predicting human bioavailability from pig data, there is considerable variability in the data set, most likely reflecting species specific differences in individual drug metabolism. Nonetheless, the correlation between pigs vs. humans was comparable to that reported for dogs vs. humans. The presented data demonstrate the suitability of the pig as a preclinical model to predict bioavailability in human.

Food for thought: formulating away the food effect - a PEARRL review.

OBJECTIVES: Co-ingestion of oral dosage forms with meals can cause substantial changes in bioavailability relative to the fasted state. Food-mediated effects on bioavailability can have significant consequences in drug development, regulatory and clinical settings. To date, the primary focus of research has focused on the ability to mechanistically understand the causes and predict the occurrence of these effects. KEY FINDINGS: The current review describes the mechanisms underpinning the occurrence of food effects, sheds new insights on the relative frequency for newly licensed medicines and describes the various methods by which they can be overcome. Analysis of oral medicines licensed by either the EMA or FDA since 2010 revealed that over 40% display significant food effects. Due to altered bioavailability, these medicines are often required to be dosed, rather restrictively, in either the fed or the fasted state, which can hinder clinical usefulness. SUMMARY: There are clinical and commercial advantages to predicting the presence of food effects early in the drug development process, in order to mitigate this risk of variable food effect bioavailability. Formulation approaches aimed at reducing variable food-dependent bioavailability, through the use of bio-enabling formulations, are an essential tool in addressing this challenge and the latest state of the art in this field are summarised here.

Mesoporous silica-based dosage forms improve bioavailability of poorly soluble drugs in pigs: case example fenofibrate.

OBJECTIVES: Mesoporous silicas (SLC) have demonstrated considerable potential to improve bioavailability of poorly soluble drugs by facilitating rapid dissolution and generating supersaturation. The addition of certain polymers can further enhance the dissolution of these formulations by preventing drug precipitation. This study uses fenofibrate as a model drug to investigate the performance of an SLC-based formulation, delivered with hydroxypropyl methylcellulose acetate succinate (HPMCAS) as a precipitation inhibitor, in pigs. The ability of biorelevant dissolution testing to predict the in vivo performance was also assessed. KEY FINDINGS: Fenofibrate-loaded mesoporous silica (FF-SLC), together with HPMCAS, displayed significant improvements in biorelevant dissolution tests relative to a reference formulation consisting of a physical mixture of crystalline fenofibrate with HPMCAS. In vivo assessment in fasted pigs demonstrated bioavailabilities of 86.69 ± 35.37% with combination of FF-SLC and HPMCAS in capsule form and 75.47 ± 14.58% as a suspension, compared to 19.92 ± 9.89% with the reference formulation. A positive correlation was identified between bioavailability and dissolution efficiency. CONCLUSIONS: The substantial improvements in bioavailability of fenofibrate from the SLC-based formulations confirm the ability of this formulation strategy to overcome the dissolution and solubility limitations, further raising the prospects of a future commercially available SLC-based formulation.

In vitro dissolution models for the prediction of in vivo performance of an oral mesoporous silica formulation.

Drug release from mesoporous silica systems has been widely investigated in vitro using USP Type II (paddle) dissolution apparatus. However, it is not clear if the observed enhanced in vitro dissolution can forecast drug bioavailability in vivo. In this study, the ability of different in vitro dissolution models to predict in vivo oral bioavailability in a pig model was examined. The fenofibrate-loaded mesoporous silica formulation was compared directly to a commercial reference product, Lipantil Supra®. Three in vitro dissolution methods were considered; USP Type II (paddle) apparatus, USP Type IV (flow-through cell) apparatus and a USP IV Transfer model (incorporating a SGF to FaSSIF-V2 media transfer). In silico modelling, using a physiologically based pharmacokinetic modelling and simulation software package (Gastroplus™), to generate in vitro/in vivo relationships, was also investigated. The study demonstrates that the in vitro dissolution performance of a mesoporous silica formulation varies depending on the dissolution apparatus utilised and experimental design. The findings show that the USP IV transfer model was the best predictor of in vivo bioavailability. The USP Type II (paddle) apparatus was not effective at forecasting in vivo behaviour. This observation is likely due to hydrodynamic differences between the two apparatus and the ability of the transfer model to better simulate gastrointestinal transit. The transfer model is advantageous in forecasting in vivo behaviour for formulations which promote drug supersaturation and as a result are prone to precipitation to a more energetically favourable, less soluble form. The USP IV transfer model could prove useful in future mesoporous silica formulation development. In silico modelling has the potential to assist in this process. However, further investigation is required to overcome the limitations of the model for solubility enhancing formulations.

Folate-targeted amphiphilic cyclodextrin.siRNA nanoparticles for prostate cancer therapy exhibit PSMA mediated uptake, therapeutic gene silencing in vitro and prolonged circulation in vivo.

In this study, a folate targeted cyclodextrin (CD) nanoparticle was prepared by co-formulating CD.siRNA complexes with DSPE-PEG5000-folate to target the prostate specific membrane antigen (PSMA). Targeted formulations showed increased uptake, relative to untargeted controls, in two prostate cancer cell lines expressing PSMA (VCaP and LNCaP). Competitive uptake studies, using excess folate, significantly reduced uptake of targeted nanoparticles in PSMA positive cell lines (P<0.001). Relative to untreated controls, folate-targeted nanoparticles significantly reduced the levels of RelA mRNA in VCaP and LNCaP cells by 44% and 22% respectively (P<0.001). In contrast there was no significant reduction in RelA mRNA in these cell lines by untargeted complexes. Pharmacokinetic (PK) data indicated that the incorporation of PEG into the formulation increased the circulation time of siRNA 8-fold. This study highlights the ability of incorporating a folate ligand into CD.siRNA nanoparticles to allow for targeted delivery of siRNA to prostate cancer cells via the PSMA.