State-of-the-art and future perspectives in ingestible remotely controlled smart capsules for drug delivery: A GENEGUT review.

An emerging concern globally, particularly in developed countries, is the rising prevalence of Inflammatory Bowel Disease (IBD), such as Crohn's disease. Oral delivery technologies that can release the active therapeutic cargo specifically at selected sites of inflammation offer great promise to maximise treatment outcomes and minimise off-target effects. Therapeutic strategies for IBD have expanded in recent years, with an increasing focus on biologic and nucleic acid-based therapies. Reliable site-specific delivery in the gastrointestinal (GI) tract is particularly crucial for these therapeutics to ensure sufficient concentrations in the targeted cells. Ingestible smart capsules hold great potential for precise drug delivery. Despite previous unsuccessful endeavours to commercialise drug delivery smart capsules, the current rise in demand and recent advancements in component development, manufacturing, and miniaturisation have reignited interest in ingestible devices. Consequently, this review analyses the advancements in various mechanical and electrical components associated with ingestible smart drug delivery capsules. These components include modules for device localisation, actuation and retention within the GI tract, signal transmission, drug release, power supply, and payload storage. Challenges and constraints associated with previous capsule design functionality are presented, followed by a critical outlook on future design considerations to ensure efficient and reliable site-specific delivery for the local treatment of GI disorders.

Predictions of biorelevant solubility change during dispersion and digestion of lipid-based formulations.

Computational approaches are increasingly explored in development of drug products, including the development of lipid-based formulations (LBFs), to assess their feasibility for achieving adequate oral absorption at an early stage. This study investigated the use of computational pharmaceutics approaches to predict solubility changes of poorly soluble drugs during dispersion and digestion in biorelevant media. Concentrations of 30 poorly water-soluble drugs were determined pre- and post-digestion with in-line UV probes using the MicroDISS Profiler™. Generally, cationic drugs displayed higher drug concentrations post-digestion, whereas for non-ionized drugs there was no discernible trend between drug concentration in dispersed and digested phase. In the case of anionic drugs there tended to be a decrease or no change in the drug concentration post-digestion. Partial least squares modelling was used to identify the molecular descriptors and drug properties which predict changes in solubility ratio in long-chain LBF pre-digestion (R2 of calibration = 0.80, Q2 of validation = 0.64) and post-digestion (R2 of calibration = 0.76, Q2 of validation = 0.72). Furthermore, multiple linear regression equations were developed to facilitate prediction of the solubility ratio pre- and post-digestion. Applying three molecular descriptors (melting point, LogD, and number of aromatic rings) these equations showed good predictivity (pre-digestion R2 = 0.70, and post-digestion R2 = 0.68). The model developed will support a computationally guided LBF strategy for emerging poorly water-soluble drugs by predicting biorelevant solubility changes during dispersion and digestion. This facilitates a more data-informed developability decision making and subsequently facilitates a more efficient use of formulation screening resources.

Predictive computational models for assessing the impact of co-milling on drug dissolution.

Co-milling is an effective technique for improving dissolution rate limited absorption characteristics of poorly water-soluble drugs. However, there is a scarcity of models available to forecast the magnitude of dissolution rate improvement caused by co-milling. Therefore, this study endeavoured to quantitatively predict the increase in dissolution by co-milling based on drug properties. Using a biorelevant dissolution setup, a series of 29 structurally diverse and crystalline drugs were screened in co-milled and physically blended mixtures with Polyvinylpyrrolidone K25. Co-Milling Dissolution Ratios after 15 min (COMDR15 min) and 60 min (COMDR60 min) drug release were predicted by variable selection in the framework of a partial least squares (PLS) regression. The model forecasts the COMDR15 min (R2 = 0.82 and Q2 = 0.77) and COMDR60 min (R2 = 0.87 and Q2 = 0.84) with small differences in root mean square errors of training and test sets by selecting four drug properties. Based on three of these selected variables, applicable multiple linear regression equations were developed with a high predictive power of R2 = 0.83 (COMDR15 min) and R2 = 0.84 (COMDR60 min). The most influential predictor variable was the median drug particle size before milling, followed by the calculated drug logD6.5 value, the calculated molecular descriptor Kappa 3 and the apparent solubility of drugs after 24 h dissolution. The study demonstrates the feasibility of forecasting the dissolution rate improvements of poorly water-solube drugs through co-milling. These models can be applied as computational tools to guide formulation in early stage development.

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.

Comparative Analysis of Chemical Descriptors by Machine Learning Reveals Atomistic Insights into Solute-Lipid Interactions.

This study explores the research area of drug solubility in lipid excipients, an area persistently complex despite recent advancements in understanding and predicting solubility based on molecular structure. To this end, this research investigated novel descriptor sets, employing machine learning techniques to understand the determinants governing interactions between solutes and medium-chain triglycerides (MCTs). Quantitative structure-property relationships (QSPR) were constructed on an extended solubility data set comprising 182 experimental values of structurally diverse drug molecules, including both development and marketed drugs to extract meaningful property relationships. Four classes of molecular descriptors, ranging from traditional representations to complex geometrical descriptions, were assessed and compared in terms of their predictive accuracy and interpretability. These include two-dimensional (2D) and three-dimensional (3D) descriptors, Abraham solvation parameters, extended connectivity fingerprints (ECFPs), and the smooth overlap of atomic position (SOAP) descriptor. Through testing three distinct regularized regression algorithms alongside various preprocessing schemes, the SOAP descriptor enabled the construction of a superior performing model in terms of interpretability and accuracy. Its atom-centered characteristics allowed contributions to be estimated at the atomic level, thereby enabling the ranking of prevalent molecular motifs and their influence on drug solubility in MCTs. The performance on a separate test set demonstrated high predictive accuracy (RMSE = 0.50) for 2D and 3D, SOAP, and Abraham Solvation descriptors. The model trained on ECFP4 descriptors resulted in inferior predictive accuracy. Lastly, uncertainty estimations for each model were introduced to assess their applicability domains and provide information on where the models may extrapolate in chemical space and, thus, where more data may be necessary to refine a data-driven approach to predict solubility in MCTs. Overall, the presented approaches further enable computationally informed formulation development by introducing a novel in silico approach for rational drug development and prediction of dose loading in lipids.

Digestion of surfactants does not affect their ability to inhibit P-gp-mediated transport in vitro.

While various non-ionic surfactants at low concentrations have been shown to increase the transport of P-gp substrates in vitro, in vivo studies in rats have shown that a higher surfactant concentration is needed to increase the oral absorption of e.g. the P-gp substrates digoxin and etoposide. The aim of the present study was to investigate if intestinal digestion of surfactants could be the reason for this deviation between in vitro and in vivo data. Therefore, Kolliphor EL, Brij-L23, Labrasol and polysorbate 20 were investigated for their ability to inhibit P-gp and increase digoxin absorption in vitro. Transport studies were performed in Caco-2 cells, while P-gp inhibition and cell viability assays were performed in MDCKII-MDR1 cells. Polysorbate 20, Kolliphor EL and Brij-L23 increased absorptive transport and decreased secretory digoxin transport in Caco-2 cells, whereas only polysorbate 20 and Brij-L23 showed P-gp inhibiting properties in the MDCKII-MDR1 cells. Polysorbate 20 and Brij-L23 were chosen for in vitro digestion prior to transport- or P-gp inhibiting assays. Brij-L23 was not digestible, whereas polysorbate 20 reached a degree of digestion around 40%. Neither of the two surfactants showed any significant difference in their ability to affect absorptive or secretory transport of digoxin after pre-digestion. Furthermore, the P-gp inhibiting effects of polysorbate 20 were not decreased significantly. In conclusion, the mechanism behind the non-ionic surfactant mediated in vitro P-gp inhibition seemed independent of the intestinal digestion and the results presented here did not suggest it to be the cause of the observed discrepancy between in vitro and in vivo.

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.

Developing an in vitro lipolysis model for real-time analysis of drug concentrations during digestion of lipid-based formulations.

Understanding the effect of digestion on oral lipid-based drug formulations is a critical step in assessing the impact of the digestive process in the intestine on intraluminal drug concentrations. The classical pH-stat in vitro lipolysis technique has traditionally been applied, however, there is a need to explore the establishment of higher throughput small-scale methods. This study explores the use of alternative lipases with the aim of selecting digestion conditions that permit in-line UV detection for the determination of real-time drug concentrations. A range of immobilised and pre-dissolved lipases were assessed for digestion of lipid-based formulations and compared to digestion with the classical source of lipase, porcine pancreatin. Palatase® 20000 L, a purified liquid lipase, displayed comparable digestion kinetics to porcine pancreatin and drug concentration determined during digestion of a fenofibrate lipid-based formulation were similar between methods. In-line UV analysis using the MicroDISS ProfilerTM demonstrated that drug concentration could be monitored during one hour of dispersion and three hours of digestion for both a medium- and long-chain lipid-based formulations with corresponding results to that obtained from the classical lipolysis method. This method offers opportunities exploring the real-time dynamic drug concentration during dispersion and digestion of lipid-based formulations in a small-scale setup avoiding artifacts as a result of extensive sample preparation.

Lipid based formulations as supersaturating oral delivery systems: From current to future industrial applications.

Lipid-based formulations, in particular supersaturated lipid-based formulations, are important delivery approaches when formulating challenging compounds, as especially low water-soluble compounds profit from delivery in a pre-dissolved state. In this article, the classification of lipid-based formulation is described, followed by a detailed discussion of different supersaturated lipid-based formulations and the recent advances reported in the literature. The supersaturated lipid-based formulations discussed include both the in situ forming supersaturated systems as well as the thermally induced supersaturated lipid-based formulations. The in situ forming drug supersaturation by lipid-based formulations has been widely employed and numerous clinically available products are on the market. There are some scientific gaps in the field, but in general there is a good understanding of the mechanisms driving the success of these systems. For thermally induced supersaturation, the technology is not yet fully understood and developed, hence more research is required in this field to explore the formulations beyond preclinical studies and initial clinical trials.

Advancing algorithmic drug product development: Recommendations for machine learning approaches in drug formulation.

Artificial intelligence is a rapidly expanding area of research, with the disruptive potential to transform traditional approaches in the pharmaceutical industry, from drug discovery and development to clinical practice. Machine learning, a subfield of artificial intelligence, has fundamentally transformed in silico modelling and has the capacity to streamline clinical translation. This paper reviews data-driven modelling methodologies with a focus on drug formulation development. Despite recent advances, there is limited modelling guidance specific to drug product development and a trend towards suboptimal modelling practices, resulting in models that may not give reliable predictions in practice. There is an overwhelming focus on benchtop experimental outcomes obtained for a specific modelling aim, leaving the capabilities of data scraping or the use of combined modelling approaches yet to be fully explored. Moreover, the preference for high accuracy can lead to a reliance on black box methods over interpretable models. This further limits the widespread adoption of machine learning as black boxes yield models that cannot be easily understood for the purposes of enhancing product performance. In this review, recommendations for conducting machine learning research for drug product development to ensure trustworthiness, transparency, and reliability of the models produced are presented. Finally, possible future directions on how research in this area might develop are discussed to aim for models that provide useful and robust guidance to formulators.

Leveraging the use of in vitro and computational methods to support the development of enabling oral drug products: An InPharma commentary.

Due to the strong tendency towards poorly soluble drugs in modern development pipelines, enabling drug formulations such as amorphous solid dispersions, cyclodextrins, co-crystals and lipid-based formulations are frequently applied to solubilize or generate supersaturation in gastrointestinal fluids, thus enhancing oral drug absorption. Although many innovative in vitro and in silico tools have been introduced in recent years to aid development of enabling formulations, significant knowledge gaps still exist with respect to how best to implement them. As a result, the development strategy for enabling formulations varies considerably within the industry and many elements of empiricism remain. The InPharma network aims to advance a mechanistic, animal-free approach to the assessment of drug developability. This commentary focuses current status and next steps that will be taken in InPharma to identify and fully utilize 'best practice' in vitro and in silico tools for use in physiologically based biopharmaceutic models.

P-glycoprotein-mediated transport in a mucus-supplemented Caco-2 cell model in the presence of different surfactants.

The aim of the present study was to investigate if mucus applied to Caco-2 cell monolayers protects cells from high concentrations of surfactants, while still allowing for an identification of the surfactant's inhibitory effects on P-glycoprotein (P-gp). Two types of porcine mucin and six surfactants (Polysorbate 20 (PS20) and 80 (PS80), Kolliphor EL (Kol. EL) and RH40 (Kol. RH40), Labrafil M 2125 CS (L.fil) and Labrasol (L.sol)) were applied to Caco-2 cells, and TEER, paracellular transport and P-gp mediated digoxin transport was measured. The results showed that 15% porcine mucin type II was incompatible with Caco-2 cell monolayer integrity, resulting in a dramatic drop in monolayer TEER and increased mannitol transport. In contrast, mucin type III was compatible with Caco-2 cell monolayers in the concentration range of 2.5-15% without substantially disturbing barrier properties. The highest concentration of mucin type III impaired the ability of all six surfactants to decrease P-gp mediated digoxin transport. Subsequently lowering the mucin concentration to 5% facilitated adequate protection of cells and enabled e.g., 5% PS20 to inhibit P-gp mediated digoxin transport. Overall, the present work is useful for early-stage permeability investigations on how mucus affects P-gp mediated transport in the presence of formulation excipients.

Developing Clinically Relevant Dissolution Specifications (CRDSs) for Oral Drug Products: Virtual Webinar Series.

A webinar series that was organised by the Academy of Pharmaceutical Sciences Biopharmaceutics focus group in 2021 focused on the challenges of developing clinically relevant dissolution specifications (CRDSs) for oral drug products. Industrial scientists, together with regulatory and academic scientists, came together through a series of six webinars, to discuss progress in the field, emerging trends, and areas for continued collaboration and harmonisation. Each webinar also hosted a Q&A session where participants could discuss the shared topic and information. Although it was clear from the presentations and Q&A sessions that we continue to make progress in the field of CRDSs and the utility/success of PBBM, there is also a need to continue the momentum and dialogue between the industry and regulators. Five key areas were identified which require further discussion and harmonisation.

Gut microbiota-drug interactions in cancer pharmacotherapies: implications for efficacy and adverse effects.

INTRODUCTION: The gut microbiota is involved in host physiology and health. Reciprocal microbiota-drug interactions are increasingly recognized as underlying some individual differences in therapy response and adverse events. Cancer pharmacotherapies are characterized by a high degree of interpatient variability in efficacy and side effect profile and recently, the microbiota has emerged as a factor that may underlie these differences. AREAS COVERED: The effects of cancer pharmacotherapy on microbiota composition and function are reviewed with consideration of the relationship between baseline microbiota composition, microbiota modification, antibiotics exposure, and cancer therapy efficacy. We assess the evidence implicating the microbiota in cancer therapy-related adverse events including impaired gut function, cognition, and pain perception. Finally, potential mechanisms underlying microbiota-cancer drug interactions are described, including direct microbial metabolism, and microbial modulation of liver metabolism and immune function. This review focused on preclinical and clinical studies conducted in the last 5 years. EXPERT OPINION: Preclinical and clinical research supports a role for baseline microbiota in cancer therapy efficacy, with emerging evidence that the microbiota modification may assist in side effect management. Future efforts should focus on exploiting this knowledge toward the development of microbiota-targeted therapies. Finally, a focus on specific drug-microbiota-cancer interactions is warranted.

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.

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.

Lipophilic Salts and Lipid-Based Formulations for Bridging the Food Effect Gap of Venetoclax.

Lipid based formulations (LBF) have shown to overcome food dependent bioavailability for some poorly water-soluble drugs. However, the utility of LBFs can be limited by low dose loading due to a low drug solubility in LBF vehicles. This study investigated the solubility and drug loading increases in LBFs using lipophilic counterions to form lipophilic salts of venetoclax. Venetoclax docusate was formed from venetoclax free base and verified by 1H NMR. Formation of stable venetoclax-fatty acid associations with either oleic acid or decanoic acid were attempted, however, the molecular associations were less consistent based on 1H NMR. Venetoclax docusate displayed a up to 6.2-fold higher solubility in self-emulsifying drug delivery systems (SEDDS) when compared to the venetoclax free base solubility resulting in a higher dose loading. A subsequent bioavailability study in landrace pigs demonstrated a 2.5-fold higher bioavailability for the lipophilic salt containing long chain SEDDS compared to the commercially available solid dispersion Venclyxto® in the fasted state. The bioavailability of all lipophilic salt SEDDS in the fasted state was similar to Venclyxto® in the fed state. This study confirmed that lipophilic drug salts increase the dose loading in LBFs and showed that lipophilic salt-SEDDS combinations may be able to overcome bioavailability limitations of drugs with low inherent dose loading in lipid vehicles. Furthermore, the present study demonstrated the utility of a LBF approach, in combination with lipophilic salts, to overcome food dependent variable oral bioavailability of drugs.

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.

Synthesis and In Vivo Evaluation of Insulin-Loaded Whey Beads as an Oral Peptide Delivery System.

For many diabetics, daily, lifelong insulin injections are required to effectively manage blood glucose levels and the complications associated with the disease. This can be a burden and reduces patient quality of life. Our goal was to develop a more convenient oral delivery system that may be suitable for insulin and other peptides. Insulin was entrapped in 1.5-mm beads made from denatured whey protein isolate (dWPI) using gelation. Beads were then air-dried with fumed silica, Aerosil®. The encapsulation efficiency was ~61% and the insulin loading was ~25 µg/mg. Dissolution in simulated gastric-, and simulated intestinal fluids (SGF, SIF) showed that ~50% of the insulin was released from beads in SGF, followed by an additional ~10% release in SIF. The omission of Aerosil® allowed greater insulin release, suggesting that it formed a barrier on the bead surface. Circular dichroism analysis of bead-released insulin revealed an unaltered secondary structure, and insulin bioactivity was retained in HepG2 cells transfected to assess activation of the endogenous insulin receptors. Insulin-entrapped beads were found to provide partial protection against pancreatin for at least 60 min. A prototype bead construct was then synthesised using an encapsulator system and tested in vivo using a rat intestinal instillation bioassay. It was found that 50 IU/kg of entrapped insulin reduced plasma glucose levels by 55% in 60 min, similar to that induced by subcutaneously (s.c.)-administered insulin (1 IU/kg). The instilled insulin-entrapped beads produced a relative bioavailability of 2.2%. In conclusion, when optimised, dWPI-based beads may have potential as an oral peptide delivery system.

Oral biopharmaceutics tools: recent progress from partnership through the Pharmaceutical Education and Research with Regulatory Links collaboration.

OBJECTIVES: To summarise key contributions of the Pharmaceutical Education and Research with Regulatory Links (PEARRL) project (2016-2020) to the optimisation of existing and the development of new biopharmaceutics tools for evaluating the in vivo performance of oral drug products during the development of new drugs and at the regulatory level. KEY FINDINGS: Optimised biopharmaceutics tools: Based on new clinical data, the composition of biorelevant media for simulating the fed state conditions in the stomach was simplified. Strategies on how to incorporate biorelevant in vitro data of bio-enabling drug products into physiologically based pharmacokinetic (PBPK) modelling were proposed. Novel in vitro biopharmaceutics tools: Small-scale two-stage biphasic dissolution and dissolution-permeation setups were developed to facilitate understanding of the supersaturation effects and precipitation risks of orally administered drugs. A porcine fasted state simulated intestinal fluid was developed to improve predictions and interpretation of preclinical results using in vitro dissolution studies. Based on new clinical data, recommendations on the design of in vitro methodologies for evaluating the GI drug transfer process in the fed state were suggested. The optimized design of in vivo studies for investigating food effects: A food effect study protocol in the pig model was established which successfully predicted the food-dependent bioavailability of two model compounds. The effect of simulated infant fed state conditions in healthy adults on the oral absorption of model drugs was evaluated versus the fasted state and the fed state conditions, as defined by regulatory agencies for adults. Using PBPK modelling, the extrapolated fasted and infant fed conditions data appeared to be more useful to describe early drug exposure in infants, while extrapolation of data collected under fed state conditions, as defined by regulators for adults, failed to capture in vivo infant drug absorption. SUMMARY: Substantial progress has been made in developing an advanced suite of biopharmaceutics tools for streamlining drug formulation screening and supporting regulatory applications. These advances in biopharmaceutics were achieved through networking opportunities and research collaborations provided under the H2020 funded PEARRL project.