Re-discover the value of protein binding assessments in hepatic and renal impairment studies and its contributions in drug labels and dose decisions.

One of the key pharmacokinetic properties of most small molecule drugs is their ability to bind to serum proteins. Unbound or free drug is responsible for pharmacological activity while the balance between free and bound drug can impact drug distribution, elimination, and other safety parameters. In the hepatic impairment (HI) and renal impairment (RI) clinical studies, unbound drug concentration is often assessed; however, the relevance and impact of the protein binding (PB) results is largely limited. We analyzed published clinical safety and pharmacokinetic studies in subjects with HI or RI with PB assessment up to October 2022 and summarized the contribution of PB results on their label dose recommendations. Among drugs with HI publication, 32% (17/53) associated product labels include PB results in HI section. Of these, the majority (9/17, 53%) recommend dose adjustments consistent with observed PB change. Among drugs with RI publication, 27% (12/44) of associated product labels include PB results in RI section with the majority (7/12, 58%) recommending no dose adjustment, consistent with the reported absence of PB change. PB results were found to be consistent with a tailored dose recommendation in 53% and 58% of the approved labels for HI and RI section, respectively. We further discussed the interpretation challenges of PB results, explored treatment decision factors including total drug concentration, exposure-response relationships, and safety considerations in these case examples. Collectively, comprehending the alterations in free drug levels in HI and RI informs treatment decision through a risk-based approach.

Impact of Obesity on Hepatic Drug Clearance: What are the Influential Variables?

Drug clearance in obese subjects varies widely among different drugs and across subjects with different severity of obesity. This study investigates correlations between plasma clearance (CLp) and drug- and patient-related characteristics in obese subjects, and evaluates the systematic accuracy of common weight-based dosing methods. A physiologically-based pharmacokinetic (PBPK) modeling approach that uses recent information on obesity-related changes in physiology was used to simulate CLp for a normal-weight subject (body mass index [BMI] = 20) and subjects with various severities of obesity (BMI 25-60) for hypothetical hepatically cleared drugs with a wide range of properties. Influential variables for CLp change were investigated. For each drug and obese subject, the exponent that yields perfect allometric scaling of CLp from normal-weight subjects was assessed. Among all variables, BMI and relative changes in enzyme activity resulting from obesity proved highly correlated with obesity-related CLp changes. Drugs bound to α1-acid glycoprotein (AAG) had lower CLp changes compared to drugs bound to human serum albumin (HSA). Lower extraction ratios (ER) corresponded to higher CLp changes compared to higher ER. The allometric exponent for perfect scaling ranged from -3.84 to 3.34 illustrating that none of the scaling methods performed well in all situations. While all three dosing methods are generally systematically accurate for drugs with unchanged or up to 50% increased enzyme activity in subjects with a BMI below 30 kg/m2, in any of the other cases, information on the different drug properties and severity of obesity is required to select an appropriate dosing method for individuals with obesity.

Insights into Inhalation Drug Disposition: The Roles of Pulmonary Drug-Metabolizing Enzymes and Transporters.

The past five decades have witnessed remarkable advancements in the field of inhaled medicines targeting the lungs for respiratory disease treatment. As a non-invasive drug delivery route, inhalation therapy offers numerous benefits to respiratory patients, including rapid and targeted exposure at specific sites, quick onset of action, bypassing first-pass metabolism, and beyond. Understanding the characteristics of pulmonary drug transporters and metabolizing enzymes is crucial for comprehending efficient drug exposure and clearance processes within the lungs. These processes are intricately linked to both local and systemic pharmacokinetics and pharmacodynamics of drugs. This review aims to provide a comprehensive overview of the literature on lung transporters and metabolizing enzymes while exploring their roles in exogenous and endogenous substance disposition. Additionally, we identify and discuss the principal challenges in this area of research, providing a foundation for future investigations aimed at optimizing inhaled drug administration. Moving forward, it is imperative that future research endeavors to focus on refining and validating in vitro and ex vivo models to more accurately mimic the human respiratory system. Such advancements will enhance our understanding of drug processing in different pathological states and facilitate the discovery of novel approaches for investigating lung-specific drug transporters and metabolizing enzymes. This deeper insight will be crucial in developing more effective and targeted therapies for respiratory diseases, ultimately leading to improved patient outcomes.

Understanding the impact of binding free energy and kinetics calculations in modern drug discovery.

INTRODUCTION: For rational drug design, it is crucial to understand the receptor-drug binding processes and mechanisms. A new era for the use of computer simulations in predicting drug-receptor interactions at an atomic level has begun with remarkable advances in supercomputing and methodological breakthroughs. AREAS COVERED: End-point free energy calculation methods such as Molecular Mechanics/Poisson Boltzmann Surface Area (MM/PBSA) or Molecular-Mechanics/Generalized Born Surface Area (MM/GBSA), free energy perturbation (FEP), and thermodynamic integration (TI) are commonly used for binding free energy calculations in drug discovery. In addition, kinetic dissociation and association rate constants (koff and kon) play critical roles in the function of drugs. Nowadays, Molecular Dynamics (MD) and enhanced sampling simulations are increasingly being used in drug discovery. Here, the authors provide a review of the computational techniques used in drug binding free energy and kinetics calculations. EXPERT OPINION: The applications of computational methods in drug discovery and design are expanding, thanks to improved predictions of the binding free energy and kinetic rates of drug molecules. Recent microsecond-timescale enhanced sampling simulations have made it possible to accurately capture repetitive ligand binding and dissociation, facilitating more efficient and accurate calculations of ligand binding free energy and kinetics.

Machine learning framework to predict pharmacokinetic profile of small molecule drugs based on chemical structure.

Accurate prediction of a new compound's pharmacokinetic (PK) profile is pivotal for the success of drug discovery programs. An initial assessment of PK in preclinical species and humans is typically performed through allometric scaling and mathematical modeling. These methods use parameters estimated from in vitro or in vivo experiments, which although helpful for an initial estimation, require extensive animal experiments. Furthermore, mathematical models are limited by the mechanistic underpinning of the drugs' absorption, distribution, metabolism, and elimination (ADME) which are largely unknown in the early stages of drug discovery. In this work, we propose a novel methodology in which concentration versus time profile of small molecules in rats is directly predicted by machine learning (ML) using structure-driven molecular properties as input and thus mitigating the need for animal experimentation. The proposed framework initially predicts ADME properties based on molecular structure and then uses them as input to a ML model to predict the PK profile. For the compounds tested, our results demonstrate that PK profiles can be adequately predicted using the proposed algorithm, especially for compounds with Tanimoto score greater than 0.5, the average mean absolute percentage error between predicted PK profile and observed PK profile data was found to be less than 150%. The suggested framework aims to facilitate PK predictions and thus support molecular screening and design earlier in the drug discovery process.

Nanomedicines for intranasal delivery: understanding the nano-bio interactions at the nasal mucus-mucosal barrier.

INTRODUCTION: Intranasal administration is an effective drug delivery routes in modern pharmaceutics. However, unlike other in vivo biological barriers, the nasal mucosal barrier is characterized by high turnover and selective permeability, hindering the diffusion of both particulate drug delivery systems and drug molecules. The in vivo fate of administrated nanomedicines is often significantly affected by nano-biointeractions. AREAS COVERED: The biological barriers that nanomedicines encounter when administered intranasally are introduced, with a discussion on the factors influencing the interaction between nanomedicines and the mucus layer/mucosal barriers. General design strategies for nanomedicines administered via the nasal route are further proposed. Furthermore, the most common methods to investigate the characteristics and the interactions of nanomedicines when in presence of the mucus layer/mucosal barrier are briefly summarized. EXPERT OPINION: Detailed investigation of nanomedicine-mucus/mucosal interactions and exploration of their mechanisms provide solutions for designing better intranasal nanomedicines. Designing and applying nanomedicines with mucus interaction properties or non-mucosal interactions should be customized according to the therapeutic need, considering the target of the drug, i.e. brain, lung or nose. Then how to improve the precise targeting efficiency of nanomedicines becomes a difficult task for further research.

Removal of pharmaceutical compounds and toxicology study in wastewater using Malaysian fungal Ganoderma lucidum.

Elevated usage of pharmaceutical products leads to the accumulation of emerging contaminants in sewage. In the current work, Ganoderma lucidum (GL) was used to remove pharmaceutical compounds (PCs), proposed as a tertiary method in sewage treatment plants (STPs). The PCs consisted of a group of painkillers (ketoprofen, diclofenac, and dexamethasone), psychiatrists (carbamazepine, venlafaxine, and citalopram), beta-blockers (atenolol, metoprolol, and propranolol), and anti-hypertensives (losartan and valsartan). The performance of 800 mL of synthetic water, effluent STP, and hospital wastewater (HWW) was evaluated. Parameters, including treatment time, inoculum volume, and mechanical agitation speed, have been tested. The toxicity of the GL after treatment is being studied based on exposure levels to zebrafish embryos (ZFET) and the morphology of the GL has been observed via Field Emission Scanning Electron Microscopy (FESEM). The findings conclude that GL can reduce PCs from <10% to >90%. Diclofenac and valsartan are the highest (>90%) in the synthetic model, while citalopram and propranolol (>80%) are in the real wastewater. GL effectively removed pollutants in 48 h, 1% of the inoculum volume, and 50 rpm. The ZFET showed GL is non-toxic (LC50 is 209.95 mg/mL). In the morphology observation, pellets GL do not show major differences after treatment, showing potential to be used for a longer treatment time and to be re-useable in the system. GL offers advantages to removing PCs in water due to their non-specific extracellular enzymes that allow for the biodegradation of PCs and indicates a good potential in real-world applications as a favourable alternative treatment.

Another string to your bow: machine learning prediction of the pharmacokinetic properties of small molecules.

INTRODUCTION: Prediction of pharmacokinetic (PK) properties is crucial for drug discovery and development. Machine-learning (ML) models, which use statistical pattern recognition to learn correlations between input features (such as chemical structures) and target variables (such as PK parameters), are being increasingly used for this purpose. To embed ML models for PK prediction into workflows and to guide future development, a solid understanding of their applicability, advantages, limitations, and synergies with other approaches is necessary. AREAS COVERED: This narrative review discusses the design and application of ML models to predict PK parameters of small molecules, especially in light of established approaches including in vitro-in vivo extrapolation (IVIVE) and physiologically based pharmacokinetic (PBPK) models. The authors illustrate scenarios in which the three approaches are used and emphasize how they enhance and complement each other. In particular, they highlight achievements, the state of the art and potentials of applying machine learning for PK prediction through a comphrehensive literature review. EXPERT OPINION: ML models, when carefully crafted, regularly updated, and appropriately used, empower users to prioritize molecules with favorable PK properties. Informed practitioners can leverage these models to improve the efficiency of drug discovery and development process.

Drugs in Human Milk Part 1: Practical and Analytical Considerations in Measuring Drugs and Metabolites in Human Milk.

Human milk is a remarkable biofluid that provides essential nutrients and immune protection to newborns. Breastfeeding women consuming medications could pass the drug through their milk to neonates. Drugs can be transferred to human milk by passive diffusion or active transport. The physicochemical properties of the drug largely impact the extent of drug transfer into human milk. A comprehensive understanding of the physiology of human milk formation, composition of milk, mechanisms of drug transfer, and factors influencing drug transfer into human milk is critical for appropriate selection and use of medications in lactating women. Quantification of drugs in the milk is essential for assessing the safety of pharmacotherapy during lactation. This can be achieved by developing specific, sensitive, and reproducible analytical methods using techniques such as liquid chromatography coupled with mass spectrometry. The present review briefly discusses the physiology of human milk formation, composition of human milk, mechanisms of drug transfer into human milk, and factors influencing transfer of drugs from blood to milk. We further expand upon and critically evaluate the existing analytical approaches/assays used for the quantification of drugs in human milk.

Parental exposure to antidepressants has lasting effects on offspring? A case study with zebrafish.

Fish have common neurotransmitter pathways with humans, exhibiting a significant degree of conservation and homology. Thus, exposure to fluoxetine makes fish potentially susceptible to biochemical and physiological changes, similarly to what is observed in humans. Over the years, several studies demonstrated the potential effects of fluoxetine on different fish species and at different levels of biological organization. However, the effects of parental exposure to unexposed offspring remain largely unknown. The consequences of 15-day parental exposure to relevant concentrations of fluoxetine (100 and 1000 ng/L) were assessed on offspring using zebrafish as a model organism. Parental exposure resulted in offspring early hatching, non-inflation of the swimming bladder, increased malformation frequency, decreased heart rate and blood flow, and reduced growth. Additionally, a significant behavioral impairment was also found (reduced startle response, basal locomotor activity, and altered non-associative learning during early stages and a negative geotaxis and scototaxis, reduced thigmotaxis, and anti-social behavior at later life stages). These behavior alterations are consistent with decreased anxiety, a significant increase in the expression of the monoaminergic genes slc6a4a (sert), slc6a3 (dat), slc18a2 (vmat2), mao, tph1a, and th2, and altered levels of monoaminergic neurotransmitters. Alterations in behavior, expression of monoaminergic genes, and neurotransmitter levels persisted until offspring adulthood. Given the high conservation of neuronal pathways between fish and humans, data show the possibility of potential transgenerational and multigenerational effects of pharmaceuticals' exposure. These results reinforce the need for transgenerational and multigenerational studies in fish, under realistic scenarios, to provide realistic insights into the impact of these pharmaceuticals.

Unveiling the influence of microaeration and sludge recirculation on enhancement of pharmaceutical removal and microbial community change of the novel anaerobic baffled biofilm - membrane bioreactor in treating building wastewater.

This research aims to conduct a comparative investigation of the role played by microaeration and sludge recirculation in the novel anaerobic baffled biofilm-membrane bioreactor (AnBB-MBR) for enhancing pharmaceutical removal from building wastewater. Three AnBB-MBRs - R1: AnBB-MBR, R2: AnBB-MBR with microaeration and R3: AnBB-MBR with microaeration and sludge recirculation - were operated simultaneously to remove Ciprofloxacin (CIP), Caffeine (CAF), Sulfamethoxazole (SMX) and Diclofenac (DCF) from real building wastewater at the hydraulic retention time (HRT) of 30 h for 115 days. From the removal profiles of the targeted pharmaceuticals in the AnBB-MBRs, it was found that the fixed-film compartment (C1) could significantly reduce the targeted pharmaceuticals. The remaining pharmaceuticals were further removed with the microaeration compartment. R2 exhibited the utmost removal efficiency for CIP (78.0 %) and DCF (40.8 %), while SMX was removed most successfully by R3 (microaeration with sludge recirculation) at 91.3 %, followed by microaeration in R2 (88.5 %). For CAF, it was easily removed by all AnBB-MBR systems (>90 %). The removal mechanisms indicate that the microaeration in R2 facilitated the adsorption of CIP onto microaerobic biomass, while the enhanced biodegradation of CAF, SMX and DCF was confirmed by batch biotransformation kinetics and the adsorption isotherms of the targeted pharmaceuticals. The microbial groups involved in biodegradation of the targeted compounds under microaeration were identified as nitrogen removal microbials (Nitrosomonas, Nitrospira, Thiobacillus, and Denitratisoma) and methanotrophs (Methylosarcina, Methylocaldum, and Methylocystis). Overall, explication of the integration of AnBB-MBR with microaeration (R2) confirmed it as a prospective technology for pharmaceutical removal from building wastewater due to its energy-efficient approach characterized by minimal aeration supply.

Sorting drug conformers in enzyme active sites: the XTB way.

In the present study, we have used the MEI196 set of interaction energies to investigate low-cost computational chemistry approaches for the calculation of binding between a molecule and its environment. Density functional theory (DFT) methods, when used with the vDZP basis set, yield good agreement with the reference energies. On the other hand, semi-empirical methods are less accurate as expected. By examining different groups of systems within MEI196 that contain species of a similar nature, we find that chemical similarity leads to cancellation of errors in the calculation of relative binding energies. Importantly, the semi-empirical method GFN1-xTB (XTB1) yields reasonable results for this purpose. We have thus further assessed the performance of XTB1 for calculating relative energies of docking poses of substrates in enzyme active sites represented by cluster models or within the ONIOM protocol. The results support the observations on error cancellation. This paves the way for the use of XTB1 in parts of large-scale virtual screening workflows to accelerate the drug discovery process.

Predicting Elimination of Small-Molecule Drug Half-Life in Pharmacokinetics Using Ensemble and Consensus Machine Learning Methods.

Half-life is a significant pharmacokinetic parameter included in the excretion phase of absorption, distribution, metabolism, and excretion. It is one of the key factors for the successful marketing of drug candidates. Therefore, predicting half-life is of great significance in drug design. In this study, we employed eXtreme Gradient Boosting (XGboost), randomForest (RF), gradient boosting machine (GBM), and supporting vector machine (SVM) to build quantitative structure-activity relationship (QSAR) models on 3512 compounds and evaluated model performance by using root-mean-square error (RMSE), R2, and mean absolute error (MAE) metrics and interpreted features by SHapley Additive exPlanation (SHAP). Furthermore, we developed consensus models through integrating four individual models and validated their performance using a Y-randomization test and applicability domain analysis. Finally, matched molecular pair analysis was used to extract the transformation rules. Our results revealed that XGboost outperformed other individual models (RMSE = 0.176, R2 = 0.845, MAE = 0.141). The consensus model integrating all four models continued to enhance prediction performance (RMSE = 0.172, R2 = 0.856, MAE = 0.138). We evaluated the reliability, robustness, and generalization ability via Y-randomization test and applicability domain analysis. Meanwhile, we utilized SHAP to interpret features and employed matched molecular pair analysis to extract chemical transformation rules that provide suggestions for optimizing drug structure. In conclusion, we believe that the consensus model developed in this study serve as a reliable tool to evaluate half-life in drug discovery, and the chemical transformation rules concluded in this study could provide valuable suggestions in drug discovery.

Development of Novel Methods for QSAR Modeling by Machine Learning Repeatedly: A Case Study on Drug Distribution to Each Tissue.

Artificial intelligence is expected to help identify excellent candidates in drug discovery. However, we face a lack of data, as it is time-consuming and expensive to acquire raw data perfectly for many compounds. Hence, we tried to develop a novel quantitative structure-activity relationship (QSAR) method to predict a parameter more precisely from an incomplete data set via optimizing data handling by making use of predicted explanatory variables. As a case study we focused on the tissue-to-plasma partition coefficient (Kp), which is an important parameter for understanding drug distribution in tissues and building the physiologically based pharmacokinetic model and is a representative of small and sparse data sets. In this study, we predicted the Kp values of 119 compounds in nine tissues (adipose, brain, gut, heart, kidney, liver, lung, muscle, and skin), although some of these were not available. To fill the missing values in Kp for each tissue, first we predicted those Kp values by the nonmissing data set using a random forest (RF) model with in vitro parameters (log P, fu, Drug Class, and fi) like a classical prediction by a QSAR model. Next, to predict the tissue-specific Kp values in a test data set, we constructed a second RF model with not only in vitro parameters but also the Kp values of other tissues (i.e., other than target tissues) predicted by the first RF model as explanatory variables. Furthermore, we tested all possible combinations of explanatory variables and selected the model with the highest predictability from the test data set as the final model. The evaluation of Kp prediction accuracy based on the root-mean-square error and R2 value revealed that the proposed models outperformed other machine learning methods such as the conventional RF and message-passing neural networks. Significant improvements were observed in the Kp values of adipose tissue, brain, kidney, liver, and skin. These improvements indicated that the Kp information on other tissues can be used to predict the same for a specific tissue. Additionally, we found a novel relationship between each tissue by evaluating all combinations of explanatory variables. In conclusion, we developed a novel RF model to predict Kp values. We hope that this method will be applied to various problems in the field of experimental biology which often contains missing values in the near future.

Revisiting drug-protein interaction prediction: a novel global-local perspective.

MOTIVATION: Accurate inference of potential drug-protein interactions (DPIs) aids in understanding drug mechanisms and developing novel treatments. Existing deep learning models, however, struggle with accurate node representation in DPI prediction, limiting their performance. RESULTS: We propose a new computational framework that integrates global and local features of nodes in the drug-protein bipartite graph for efficient DPI inference. Initially, we employ pre-trained models to acquire fundamental knowledge of drugs and proteins and to determine their initial features. Subsequently, the MinHash and HyperLogLog algorithms are utilized to estimate the similarity and set cardinality between drug and protein subgraphs, serving as their local features. Then, an energy-constrained diffusion mechanism is integrated into the transformer architecture, capturing interdependencies between nodes in the drug-protein bipartite graph and extracting their global features. Finally, we fuse the local and global features of nodes and employ multilayer perceptrons to predict the likelihood of potential DPIs. A comprehensive and precise node representation guarantees efficient prediction of unknown DPIs by the model. Various experiments validate the accuracy and reliability of our model, with molecular docking results revealing its capability to identify potential DPIs not present in existing databases. This approach is expected to offer valuable insights for furthering drug repurposing and personalized medicine research. AVAILABILITY AND IMPLEMENTATION: Our code and data are accessible at: https://github.com/ZZCrazy00/DPI.

Using the Dynamic Well-Stirred Model to Extrapolate Hepatic Clearance of Organic Anion-Transporting Polypeptide Transporter Substrates without Assuming Albumin-Mediated Hepatic Drug Uptake.

Extrapolating in vivo hepatic clearance from in vitro uptake data is a known challenge, especially for organic anion-transporting polypeptide transporter (OATP) substrates, and the well-stirred model (WSM) commonly yields systematic underpredictions for those anionic drugs, hypothetically due to "albumin-mediated hepatic drug uptake". In the present study, we demonstrate that the WSM incorporating the dynamic free fraction (f D), a measure of drug protein binding affinity, performs reasonably well in predicting hepatic clearance of OATP substrates. For a selection of anionic drugs, including atorvastatin, fluvastatin, pravastatin, rosuvastatin, pitavastatin, cerivastatin, and repaglinide, this dynamic well-stirred model (dWSM) correctly predicts hepatic plasma clearance within 2-fold error for six out of seven OATP substrates examined. The geometric mean of clearance ratios between the predicted and the observed values falls in the range of 1.21-1.38. As expected, the WSM with unbound fraction (f u) systematically underpredicts hepatic clearance with greater than 2-fold error for five out of seven drugs, and the geometric mean of clearance ratios between the predicted and the observed values is in the range of 0.20-0.29. The results suggest that, despite its simplicity, the dWSM operates well for transporter-mediated uptake clearance, and that clearance under-prediction of OATP substrates may not necessarily be associated with the chemical class of the anionic drugs, nor is it a result of albumin-mediated hepatic drug uptake as currently hypothesized. Instead, the superior prediction power of the dWSM confirms the utility of the dynamic free fraction in clearance prediction and the importance of drug plasma binding kinetics in hepatic uptake clearance. SIGNIFICANCE STATEMENT: The traditional well-stirred model (WSM) consistently underpredicts organin anion-transporting polypeptide transporter (OATP)-mediated hepatic uptake clearance, hypothetically due to the albumin-mediated hepatic drug uptake. In this manuscript, we apply the dynamic WSM to extrapolate hepatic clearance of the OATP substrates, and our results show significant improvements in clearance prediction without assuming albumin-mediated hepatic drug uptake.

Comparison of Human Long-Term Liver Models for Clearance Prediction of Slowly Metabolized Compounds.

The accurate prediction of human clearance is an important task during drug development. The proportion of low clearance compounds has increased in drug development pipelines across the industry since such compounds may be dosed in lower amounts and at lower frequency. These type of compounds present new challenges to in vitro systems used for clearance extrapolation. In this study, we compared the accuracy of clearance predictions of suspension culture to four different long-term stable in vitro liver models, including HepaRG sandwich culture, the Hµrel stochastic co-culture, the Hepatopac micropatterned co-culture (MPCC), and a micro-array spheroid culture. Hepatocytes in long-term stable systems remained viable and active over several days of incubation. Although intrinsic clearance values were generally high in suspension culture, clearance of low turnover compounds could frequently not be determined using this method. Metabolic activity and intrinsic clearance values from HepaRG cultures were low and, consequently, many compounds with low turnover did not show significant decline despite long incubation times. Similarly, stochastic co-cultures occasionally failed to show significant turnover for multiple low and medium turnover compounds. Among the different methods, MPCCs and spheroids provided the most consistent measurements. Notably, all culture methods resulted in underprediction of clearance; this could, however, be compensated for by regression correction. Combined, the results indicate that spheroid culture as well as the MPCC system provide adequate in vitro tools for human extrapolation for compounds with low metabolic turnover. SIGNIFICANCE STATEMENT: In this study, we compared suspension cultures, HepaRG sandwich cultures, the Hµrel liver stochastic co-cultures, the Hepatopac micropatterned co-cultures (MPCC), and micro-array spheroid cultures for low clearance determination and prediction. Overall, HepaRG and suspension cultures showed modest value for the low determination and prediction of clearance compounds. The micro-array spheroid culture resulted in the most robust clearance measurements, whereas using the MPCC resulted in the most accurate prediction for low clearance compounds.

A novel simulated media system for in vitro evaluation of bioequivalent intestinal drug solubility.

Orally administered solid drug must dissolve in the gastrointestinal tract before absorption to provide a systemic response. Intestinal solubility is therefore crucial but difficult to measure since human intestinal fluid (HIF) is challenging to obtain, varies between fasted (Fa) and fed (Fe) states and exhibits inter and intra subject variability. A single simulated intestinal fluid (SIF) cannot reflect HIF variability, therefore current approaches are not optimal. In this study we have compared literature Fa/FeHIF drug solubilities to values measured in a novel in vitro simulated nine media system for either the fasted (Fa9SIF) or fed (Fe9SIF) state. The manuscript contains 129 literature sampled human intestinal fluid equilibrium solubility values and 387 simulated intestinal fluid equilibrium solubility values. Statistical comparison does not detect a difference (Fa/Fe9SIF vs Fa/FeHIF), a novel solubility correlation window enclosed 95% of an additional literature Fa/FeHIF data set and solubility behaviour is consistent with previous physicochemical studies. The Fa/Fe9SIF system therefore represents a novel in vitro methodology for bioequivalent intestinal solubility determination. Combined with intestinal permeability this provides an improved, population based, biopharmaceutical assessment that guides formulation development and indicates the presence of food based solubility effects. This transforms predictive ability during drug discovery and development and may represent a methodology applicable to other multicomponent fluids where no single component is responsible for performance.

Biotransformation of fluorinated drugs and xenobiotics by the model fungus Cunninghamella elegans.

Some species of the genus Cunninghamella (C. elegans, C. echinulata and C. blaskesleeana) produce the same phase I and phase II metabolites when incubated with xenobiotics as mammals, and thus are considered microbial models of mammalian metabolism. This had made these fungi attractive for metabolism studies with drugs, pesticides and environmental pollutants. As a substantial proportion of pharmaceuticals and agrochemicals are fluorinated, their biotransformation has been studied in Cunninghamella fungi and C. elegans in particular. This article details the methods employed for cultivating the fungi in planktonic and biofilm cultures, and extraction and analysis of fluorinated metabolites. Furthermore, protocols for the heterologous expression of Cunninghamella cytochromes P450 (CYPs), which are the enzymes associated with phase I metabolism, are described.