A quantitative approach to probe the dependence and correlation of food-effect with aqueous solubility, dose/solubility ratio, and partition coefficient (LogP) for orally active drugs administered as immediate-release formulations

Analysis of the interplay of physiological response to food intake and drug properties in food-drug interactions

The effect of food on oral drug absorption is determined by the complex interplay among gut physiological factors and drug properties. The currently used dissolution testing and classification systems (biopharmaceutics classification system, BCS or biopharmaceutics drug disposition classification system, BDDCS) do not account for dynamic changes in gastrointestinal physiology caused by food intake. This study aimed to identify key drug properties that influence food effect (FE) using supervised machine learning approaches. The analysis showed that drugs with high logP, dose number, and extraction ratio have a higher probability of positive FE, while drugs with low permeability and high efflux saturation index have a greater likelihood of negative FE. Weakly acidic drugs also showed a greater probability of positive FE, particularly at pKa >4.3. The importance of drug properties in predicting FE was ranked as logP, dose number, extraction ratio, pKa, and permeability. The accuracy of FE prediction using the models was compared with BCS and extended clearance classification system (ECCS). Overall, the likelihood or magnitude of FE depends on physiological changes to food intake such as altered bile acid secretion rate, intestinal metabolism, transport kinetics, and gastric emptying time, which should be considered along with drug properties (e.g., solubility, logP, and ionization) in predicting FE of orally administered drugs.

Prediction of Oral Drug Absorption in Rats from In Vitro Data

PURPOSE

In drug discovery, rats are widely used for pharmacological and toxicological studies. We previously reported that a mechanism-based oral absorption model, the gastrointestinal unified theoretical framework (GUT framework), can appropriately predict the fraction of a dose absorbed (Fa) in humans and dogs. However, there are large species differences between humans and rats. The purpose of the present study was to evaluate the predictability of the GUT framework for rat Fa.

METHOD

The Fa values of 20 model drugs (a total of 39 Fa data) were predicted in a bottom-up manner. Based on the literature survey, the bile acid concentration (C_bile) and the intestinal fluid volume were set to 15 mM and 4 mL/kg, respectively, five and two times higher than in humans. LogP, pK_a, molecular weight, intrinsic solubility, bile micelle partition coefficients, and Caco-2 permeability were used as input data.

RESULTS

The Fa values were appropriately predicted for highly soluble drugs (absolute average fold error (AAFE) = 1.65, 18 Fa data) and poorly soluble drugs (AAFE = 1.57, 21 Fa data). When the species difference in C_bile was ignored, Fa was over- and under-predicted for permeability and solubility limited cases, respectively. High C_bile in rats reduces the free fraction of drug molecules available for epithelial membrane permeation while increasing the solubility of poorly soluble drugs.

CONCLUSION

The Fa values in rats were appropriately predicted by the GUT framework. This result would be of great help for a better understanding of species differences and model-informed preclinical formulation development.

Machine learning predicts the effect of food on orally administered medicines

Food-mediated changes to drug absorption, termed the food effect, are hard to predict and can have significant implications for the safety and efficacy of oral drug products in patients. Mimicking the prandial states of the human gastrointestinal tract in preclinical studies is challenging, poorly predictive and can produce difficult to interpret datasets. Machine learning (ML) has emerged from the computer science field and shows promise in interpreting complex datasets present in the pharmaceutical field. A ML-based approach aimed to predict the food effect based on an extensive dataset of over 311 drugs with more than 20 drug physicochemical properties, referred to as features. Machine learning techniques were tested; including logistic regression, support vector machine, k-Nearest neighbours and random forest. First a standard ML pipeline using a 80:20 split for training and testing was tried to predict no food effect, negative food effect and positive food effect, however this lead to specificities of less than 40%. To overcome this, a strategic ML pipeline was devised and three tasks were developed. Random forest achieved the strongest performance overall. High accuracies and sensitivities of 70%, 80% and 70% and specificities of 71%, 76% and 71% were achieved for classifying; (i) no food effect vs food effect, (ii) negative food vs positive food effect and (iii) no food effect vs negative food effect vs positive food effect, respectively. Feature importance using random forest ranked the features by importance for building the predictive tasks. The calculated dose number was the most important feature. Here, ML has provided an effective screening tool for predicting the food effect, with the potential to select lead compounds with no food effect, reduce the number of animal studies, and accelerate oral drug development studies.

Comparison of Predictions by BCS, rDCS and Machine Learning for the Effect of Food on Oral Drug Absorption Based on Features Calculated In silico

In this study, observed food effects of 473 drugs were categorized into positive, negative, or no effects and compared with the predictions made by machine learning (ML), the Biopharmaceutics Classification System (BCS) and refined Developability Classification System (rDCS). All methods used primarily in silico estimates for prediction, and for ML, four algorithms were evaluated using nested cross-validation to select important information from 371 features calculated based on the chemical structure. Approximately 18 features, including estimated solubility in biorelevant media, were selected as important, and the random forest classifier was the best among four algorithms with 36.6% error rate (ER) and 10.8% opposite prediction rate (OPR). The prediction by rDCS utilizing solubility in a biorelevant medium was somewhat inferior, but not by much; 41.0% ER and 11.4% OPR. Compared with these two methods, the prediction by BCS was inferior; 54.5% ER and 21.4% OPR. ER was improved modestly by using measured features instead of in silico estimates when BCS was applied to a subset of 151 drugs (46.4% from 55.0%). ML and rDCS predicted the food effects of the same subset using in silico estimates with ERs of 37.7% and 42.4%, respectively, suggesting that the predictions by ML and rDCS using in silico features are similar or more accurate than those by BCS using measured features. These results suggest that ML was useful in revealing essential features from complex information and, together with rDCS, is effective in predicting food effects during drug development, including early drug discovery.

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.

Factors and dosage formulations affecting the solubility and bioavailability of P-glycoprotein substrate drugs

Introduction: Expression of P-glycoprotein (P-gp) increases toward the distal small intestine, implying that the duodenum is the preferential absorption site for P-gp substrate drugs. Oral bioavailability of poorly soluble P-gp substrate drugs is low and varied but increases with high-fat meals that supply lipoidal components and bile in the duodenum.Areas covered: Absorption properties of P-gp substrate drugs along with factors and oral dosage formulations affecting their solubility and bioavailability were reviewed with PubMed literature searches. An overview is provided from the viewpoint of the 'spring-and-parachute approach' that generates supersaturation of poorly soluble P-gp substrate drugs.Expert opinion: The oral bioavailability of P-gp substrate drugs is difficult to predict because of their low solubility, preferential absorption sites, and overlapping substrate specificities with CYP3A4, along with the scattered intestinal P-gp expression/function. To attain high and steady oral bioavailability of poorly soluble P-gp substrate drugs, physicochemical modification of drugs to improve solubility, or oral dosage formulations that generate long-lasting supersaturation in the duodenum, is preferred. In particular, supersaturable lipid-based drug delivery systems that can increase passive diffusion and/or lymphatic absorption are effective and applicable to many poorly soluble P-gp substrate drugs.

Prediction of negative food effect induced by bile micelle binding on oral absorption of hydrophilic cationic drugs

The purpose of the present study was to quantitatively predict the negative food effect induced by bile micelle binding on the oral absorption of hydrophilic cationic drugs. The intrinsic membrane permeability and bile micelle unbound fraction of 12 model drugs (7 tertiary amines, 3 quaternary ammoniums, and 2 neutral drugs) were calculated from the experimental Caco-2 permeability data (P_app) under fasted and fed conditions. From these input data, the fraction of a dose absorbed (Fa) was predicted using the gastrointestinal unified theoretical framework, a mechanism-based oral absorption model. The predicted Fa ratio (fed/fasted) was then compared with the in vivo fed/fasted area under the plasma concentration-time curve ratio (AUCr). The AUCr values of tertiary amines and neutral drugs were appropriately predicted (absolute average fold error (AAFE) = 1.19), whereas those of quaternary ammoniums were markedly underestimated (AAFE = 4.70). The P_app ratio (fed/fasted) predicted AUCr less quantitatively (AAFE = 1.30 for tertiary amines and neutral drugs). The results of the present study would lead to a better understanding of negative food effect on oral drug absorption.

"Flexible-Acceptor" General Solubility Equation for beyond Rule of 5 Drugs

This study describes a novel nonlinear variant of the well-known Yalkowsky general solubility equation (GSE). The modified equation can be trained with small molecules, mostly from the Lipinski Rule of 5 (Ro5) chemical space, to predict the intrinsic aqueous solubility, S₀, of large molecules (MW > 800 Da) from beyond the rule of 5 (bRo5) space, to an accuracy almost equal to that of a recently described random forest regression (RFR) machine learning analysis. The new approach replaces the GSE constant factors in the intercept (0.5), the octanol-water log P (-1.0), and melting point, mp (-0.01) terms with simple exponential functions incorporating the sum descriptor, Φ+B (Kier Φ molecular flexibility and Abraham H-bond acceptor potential). The constants in the modified three-variable (log P, mp, Φ+B) equation were determined by partial least-squares (PLS) refinement using a small-molecule log S₀ training set (n = 6541) of mostly druglike molecules. In this "flexible-acceptor" GSE(Φ,B) model, the coefficient of log P (normally fixed at -1.0) varies smoothly from -1.1 for rigid nonionizable molecules (Φ+B = 0) to -0.39 for typically flexible (Φ ∼ 20, B ∼ 6) large molecules. The intercept (traditionally fixed at +0.5) varies smoothly from +1.9 for completely inflexible small molecules to -2.2 for typically flexible large molecules. The mp coefficient (-0.007) remains practically constant, near the traditional value (-0.01) for most molecules, which suggests that the small-to-large molecule continuum is mainly solvation responsive, apparently with only minor changes in the crystal lattice contributions. For a test set of 32 large molecules (e.g., cyclosporine A, gramicidin A, leuprolide, nafarelin, oxytocin, vancomycin, and mostly natural-product-derived therapeutics used in infectious/viral diseases, in immunosuppression, and in oncology) the modified equation predicted the intrinsic solubility with a root-mean-square error of 1.10 log unit, compared to 3.0 by the traditional GSE, and 1.07 by RFR.

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.

Impact of Dendrimers on Solubility of Hydrophobic Drug Molecules

Adequate aqueous solubility has been one of the desired properties while selecting drug molecules and other bio-actives for product development. Often solubility of a drug determines its pharmaceutical and therapeutic performance. Majority of newly synthesized drug molecules fail or are rejected during the early phases of drug discovery and development due to their limited solubility. Sufficient permeability, aqueous solubility and physicochemical stability of the drug are important for achieving adequate bioavailability and therapeutic outcome. A number of different approaches including co-solvency, micellar solubilization, micronization, pH adjustment, chemical modification, and solid dispersion have been explored toward improving the solubility of various poorly aqueous-soluble drugs. Dendrimers, a new class of polymers, possess great potential for drug solubility improvement, by virtue of their unique properties. These hyper-branched, mono-dispersed molecules have the distinct ability to bind the drug molecules on periphery as well as to encapsulate these molecules within the dendritic structure. There are numerous reported studies which have successfully used dendrimers to enhance the solubilization of poorly soluble drugs. These promising outcomes have encouraged the researchers to design, synthesize, and evaluate various dendritic polymers for their use in drug delivery and product development. This review will discuss the aspects and role of dendrimers in the solubility enhancement of poorly soluble drugs. The review will also highlight the important and relevant properties of dendrimers which contribute toward drug solubilization. Finally, hydrophobic drugs which have been explored for dendrimer assisted solubilization, and the current marketing status of dendrimers will be discussed.

Application of Physiologically Based Absorption Modeling to Characterize the Pharmacokinetic Profiles of Oral Extended Release Methylphenidate Products in Adults

A previously presented physiologically-based pharmacokinetic model for immediate release (IR) methylphenidate (MPH) was extended to characterize the pharmacokinetic behaviors of oral extended release (ER) MPH formulations in adults for the first time. Information on the anatomy and physiology of the gastrointestinal (GI) tract, together with the biopharmaceutical properties of MPH, was integrated into the original model, with model parameters representing hepatic metabolism and intestinal non-specific loss recalibrated against in vitro and in vivo kinetic data sets with IR MPH. A Weibull function was implemented to describe the dissolution of different ER formulations. A variety of mathematical functions can be utilized to account for the engineered release/dissolution technologies to achieve better model performance. The physiological absorption model tracked well the plasma concentration profiles in adults receiving a multilayer-release MPH formulation or Metadate CD, while some degree of discrepancy was observed between predicted and observed plasma concentration profiles for Ritalin LA and Medikinet Retard. A local sensitivity analysis demonstrated that model parameters associated with the GI tract significantly influenced model predicted plasma MPH concentrations, albeit to varying degrees, suggesting the importance of better understanding the GI tract physiology, along with the intestinal non-specific loss of MPH. The model provides a quantitative tool to predict the biphasic plasma time course data for ER MPH, helping elucidate factors responsible for the diverse plasma MPH concentration profiles following oral dosing of different ER formulations.

Influence of Food on Paediatric Gastrointestinal Drug Absorption Following Oral Administration: A Review

The objective of this paper was to review existing information regarding food effects on drug absorption within paediatric populations. Mechanisms that underpin food-drug interactions were examined to consider potential differences between adult and paediatric populations, to provide insights into how this may alter the pharmacokinetic profile in a child. Relevant literature was searched to retrieve information on food-drug interaction studies undertaken on: (i) paediatric oral drug formulations; and (ii) within paediatric populations. The applicability of existing methodology to predict food effects in adult populations was evaluated with respect to paediatric populations where clinical data was available. Several differences in physiology, anatomy and the composition of food consumed within a paediatric population are likely to lead to food-drug interactions that cannot be predicted based on adult studies. Existing methods to predict food effects cannot be directly extrapolated to allow predictions within paediatric populations. Development of systematic methods and guidelines is needed to address the general lack of information on examining food-drug interactions within paediatric populations.

Spectroscopic and calorimetric studies of formation of the supramolecular complexes of PAMAM G5-NH₂ and G5-OH dendrimers with 5-fluorouracil in aqueous solution

The results of spectroscopic measurements (increase in solubility, equilibrium dialysis, (1)H NMR titration) and calorimetric measurements (isothermal titration ITC) indicate exothermic (ΔH<0) and spontaneous (ΔG < 0) combination of an antitumor drug, 5-fluorouracil, by both cationic PAMAM G5-NH2 dendrimer and its hydroxyl analog PAMAM G5-OH in aqueous solutions at room temperature. PAMAM G5-NH2 dendrimer combines about 70 molecules of the drug with equilibrium constant K ≅ 300, which is accompanied by an increase in the system order (ΔS < 0). Hydroxyl dendrimer, PAMAM G5-OH, combines about 14 molecules of 5-fluorouracil with equilibrium constant K ≅ 100. This process is accompanied by an increase in the system disorder (ΔS > 0).

Food Effect in Humans: Predicting the Risk Through In Vitro Dissolution and In Vivo Pharmacokinetic Models

In vitro and in vivo experimental models are frequently used to assess a new chemical entity's (NCE) biopharmaceutical performance risk for food effect (FE) in humans. Their ability to predict human FE hinges on replicating key features of clinical FE studies and building an in vitro-in vivo relationship (IVIVR). In this study, 22 compounds that span a wide range of physicochemical properties, Biopharmaceutics Classification System (BCS) classes, and food sensitivity were evaluated for biorelevant dissolution in fasted- and fed-state intestinal media and the dog fed/fasted-state pharmacokinetic model. Using the area under the curve (AUC) as a performance measure, the ratio of the fed-to-fasted AUC (FE ratio) was used to correlate each experimental model to FE ratio in humans. A linear correlation was observed for the in vitro dissolution-human IVIVR (R (2) = 0.66, % mean square error 20.7%). Similarly, the dog FE ratio correlated linearly with the FE ratio in humans (R (2) = 0.74, % mean square error 16.25%) for 15 compounds. Data points near the correlation line indicate dissolution-driven mechanism for food effect, while deviations from the correlation line shed light on unique mechanisms that can come into play such as GI physiology or unusual physicochemical properties. In summary, fed/fasted dissolution studies and dog PK studies show a reasonable correlation to human FE, hence are useful tools to flag high-risk NCEs entering clinical development. Combining kinetic dissolution, dog FE model and in silico modeling one can study FE mechanism and formulation strategies to mitigate the FE risk.

Food-associated stimuli enhance barrier properties of gastrointestinal mucus

Orally delivered drugs and nutrients must diffuse through mucus to enter the circulatory system, but the barrier properties of mucus and their modulation by physiological factors are generally poorly characterized. The main objective of this study was to examine the impact of physicochemical changes occurring upon food ingestion on gastrointestinal (GI) mucus barrier properties. Lipids representative of postprandial intestinal contents enhanced mucus barriers, as indicated by a 10-142-fold reduction in the transport rate of 200 nm microspheres through mucus, depending on surface chemistry. Physiologically relevant increases in [Ca(2+)] resulted in a 2-4-fold reduction of transport rates, likely due to enhanced cross-linking of the mucus gel network. Reduction of pH from 6.5 to 3.5 also affected mucus viscoelasticity, reducing particle transport rates approximately 5-10-fold. Macroscopic visual observation and micro-scale lectin staining revealed mucus gel structural changes, including clumping into regions into which particles did not penetrate. Histological examination indicated food ingestion can prevent microsphere contact with and endocytosis by intestinal epithelium. Taken together, these results demonstrate that GI mucus barriers are significantly altered by stimuli associated with eating and potentially dosing of lipid-based delivery systems; these stimuli represent broadly relevant variables to consider upon designing oral therapies.

Computational prediction of drug solubility in fasted simulated and aspirated human intestinal fluid

Purpose

To develop predictive models of apparent solubility (S_app) of lipophilic drugs in fasted state simulated intestinal fluid (FaSSIF) and aspirated human intestinal fluid (HIF).

Methods

Measured S_app values in FaSSIF, HIF and phosphate buffer pH 6.5 (PhB_pH6.5) for 86 lipophilic drugs were compiled and divided into training (Tr) and test (Te) sets. Projection to latent structure (PLS) models were developed through variable selection of calculated molecular descriptors. Experimentally determined properties were included to investigate their contribution to the predictions.

Results

Modest relationships between S_app in PhB_pH6.5 and FaSSIF (R² = 0.61) or HIF (R² = 0.62) were found. As expected, there was a stronger correlation obtained between FaSSIF and HIF (R² = 0.78). Computational models were developed using calculated descriptors alone (FaSSIF, R² = 0.69 and RMSE_te of 0.77; HIF, R² = 0.84 and RMSE_te of 0.81). Accuracy improved when solubility in PhB_pH6.5 was added as a descriptor (FaSSIF, R² = 0.76 and RMSE_Te of 0.65; HIF, R² = 0.86 and RMSE_Te of 0.69), whereas no improvement was seen when melting point (Tm) or logD_{pH 6.5} were included in the models.

Conclusion

Computational models were developed, that reliably predicted S_app of lipophilic compounds in intestinal fluid, from molecular structures alone. If experimentally determined pH-dependent solubility values were available, this further improved the accuracy of the predictions.

Formulation and characterization of atovaquone nanosuspension for improved oral delivery in the treatment of malaria

Aim: The objective of the present study was to develop an atovaquone (ATQ) nanosuspension and evaluate its ability to improve the pharmacokinetic and therapeutic efficacy on oral administration. Materials & methods: The ATQ nanosuspension was prepared by a combination of microprecipitation and high-pressure homogenization. It was freeze dried and characterized for various physiochemical properties. In vivo pharmacokinetics was performed in rats whereas antimalarial efficacy was assessed in mice using a 4-day suppressive test. Results: The ATQ nanosuspension stabilized with Solutol® HS 15 (BASF India Ltd, Mumbai, India) and Capryol™ 90 (Gattefosse, Mumbai, India) exhibited a z-average diameter of 371.50 nm and a polydispersity index of 0.19. X-ray diffraction and differential scanning calorimetry analysis indicated no substantial changes in the crystalline state of ATQ nanocrystals. The aqueous solubility and in vitro dissolution rate were significantly increased by reducing the particle size. An in vivo pharmacokinetics study of the nanosuspension compared with a drug suspension and Malarone® (GlaxoSmithKline, Brentford, UK) exhibited an approximately 4.6–3.2-fold improvement in area under plasma concentration. A significant increase in Cmax and decrease in time to reach peak plasma concentration after administration was also observed. ATQ in nanosized form, even at one-quarter lower doses, exhibited greater reduction in parasitemia and prolonged survival compared with its reference formulations. Conclusion: Results of this pilot study highlight the potential of nanosuspension as an efficient and commercially viable strategy for improving delivery of ATQ for malaria treatment.

Original submitted 1 August 2011; Revised submitted 2 February 2013

Quantitative prediction of formulation-specific food effects and their population variability from in vitro data with the physiologically-based ADAM model: a case study using the BCS/BDDCS Class II drug nifedipine

Quantitative prediction of food effects (FE) upon drug pharmacokinetics, including population variability, in advance of human trials may help with trial design by optimising the number of subjects and sampling times when a clinical study is warranted or by negating the need for conduct of clinical studies. Classification and rule-based systems such as the BCS and BDDCS and statistical QSARs are widely used to anticipate the nature of FE in early drug development. However, their qualitative rather than quantitative nature makes them less appropriate for assessing the magnitude of FE. Moreover, these approaches are based upon drug properties alone and are not appropriate for estimating potential formulation-specific FE on modified or controlled release products. In contrast, physiologically-based mechanistic models can consider the scope and interplay of a range of physiological changes after food intake and, in combination with appropriate in vitro drug- and formulation-specific data, can make quantitative predictions of formulation-specific FE including the inter-individual variability of such effects. Herein the Advanced Dissolution, Absorption and Metabolism (ADAM) model is applied to the prediction of formulation-specific FE for BCS/BDDCS Class II drug and CYP3A4 substrate nifedipine using as far as possible only in vitro data. Predicted plasma concentration profiles of all three studied formulations under fasted and fed states are within 2-fold of clinically observed profiles. The % prediction error (%PE) in fed-to-fasted ratio of Cmax and AUC were less than 5% for all formulations except for the Cmax of Nifedicron (%PE=-29.6%). This successful case study should help to improve confidence in the use of mechanistic physiologically-based models coupled with in vitro data for the anticipation of FE in advance of in vivo studies. However, it is acknowledged that further studies with drugs/formulations exhibiting a wide range of properties are required to further validate this methodology.

Predictive models for drugs exhibiting negative food effects based on their biopharmaceutical characteristics

CONTEXT

A drug is defined to exhibit food effects if its pharmacokinetic parameter, area under the curve (AUC₀₋∞) is different when co-administered with food in comparison with its administration on a fasted stomach. Food effects of drugs administered in immediate release dosage forms were classified as positive, negative, and no food effects.

OBJECTIVE

In this study, predictive models for negative food effects of drugs that are stable in the gastrointestinal tract and do not complex with Ca²⁺ are reported.

METHODS

An empirical model was developed using five drugs exhibiting negative food effects and seven drugs exhibiting no food effects by multiple regression analysis, based on biopharmaceutical properties generated from in vitro experiments. An oral absorption model was adopted for simulating negative food effects of model compounds using in situ rat intestinal permeability.

RESULTS

Analysis of selected model drugs indicated that percent food effects correlated to their dissociation constant, K (K(a) or K(b)) and Caco-2 permeabilities. The obtained predictive equation was: Food effect (%)=(2.60 x 10⁵·P(app))--(2.91 x 10⁵·K)--8.50. Applying the oral absorption model, the predicted food effects matched the trends of published negative food effects when the two experimental pH conditions of fed and fasted state intestinal environment were used.

CONCLUSION

A predictive model for negative food effects based on the correlation of food effects with dissociation constant and Caco-2 permeability was established and simulations of food effects using rat intestinal permeability supported the drugs? published negative food effects. Thus, an empirical and a mechanistic model as potential tools for predicting negative food effects are reported.

Interaction between PAMAM-NH₂ G4 dendrimer and 5-fluorouracil in aqueous solution

The formation equilibrium of poly(amidoamine) dendrimer (PAMAM-NH₂ G4) complex with an oncologic drug such as 5-fluorouracil (5-FU) in water at room temperature was examined. Using the results of the drug solubility in dendrimer solutions and the method of equilibrium dialysis, the maximal number of drug molecules in the dendrimer-drug complex and its equilibrium constant were evaluated. Solubility results show that PAMAM-NH₂ G4 dendrimer can transfer tens 5-fluorouracil molecules in aqueous solution. The number of active sites in a dendrimer macromolecule being capable of combining the drug, determined by the separation method, amounts to n=30 ± 4. The calculated equilibrium constant of the 5-FU-active site bonding is equal to K=(400 ± 120).

Computational oral absorption simulation for low-solubility compounds

Bile micelles play an important role in oral absorption of low-solubility compounds. Bile micelles can affect solubility, dissolution rate, and permeability. For the pH-solubility profile in bile micelles, the Henderson-Hasselbalch equation should be modified to take bile-micelle partition into account. For the dissolution rate, in the Nernst-Brunner equation, the effective diffusion coefficient in bile-micelle media should be used instead of the monomer diffusion coefficient. The diffusion coefficient of bile micelles is 8- to 18-fold smaller than that of monomer molecules. For permeability, the effective diffusion coefficient in the unstirred water layer adjacent to the epithelial membrane, and the free fraction at the epithelial membrane surface should be taken into account. The importance of these aspects is demonstrated here using several in vivo and clinical oral-absorption data of low-solubility model compounds. Using the theoretical equations, the food effect on oral absorption is further discussed.

Current methods for predicting human food effect

Food can impact the pharmacokinetics of a drug product through several mechanisms, including but not limited to, enhancement in drug solubility, changes in GI physiology, or direct interaction with the drug. Significant food effects complicate development of new drugs, especially when clinical plans require control and/or monitoring of food intake in relation to dosing. The prediction of whether a drug or drug product will show a human food effect is challenging. In vitro models which consider physical-chemical properties can classify the potential for a compound to demonstrate a positive, negative or no food effect, and may be appropriate for screening compounds at early stages of drug discovery. When comparing various formulations, dissolution tests in biorelevant media can serve as a predictor of human drug performance under fasted and fed conditions. Few in vivo models exist which predict the magnitude of change in pharmacokinetic parameters in humans when dosing in the presence of food, with the dog appearing to be the most studied species for this purpose. Control of gastric pH, as well as the amount and composition of the fed state in dogs are critical parameters to improving the predictability of the dog overall as a food effect model. No single universal model is applicable for all drugs at all stages of drug development. One or more models may be required depending whether the goal is to assess potential for a food effect, determine the magnitude of change in pharmacokinetic parameters in the fed/fasted state, or whether formulation efforts have the ability to mitigate an observed food effect.

Predicting drug disposition, absorption/elimination/transporter interplay and the role of food on drug absorption

The ability to predict drug disposition involves concurrent consideration of many chemical and physiological variables and the effect of food on the rate and extent of availability adds further complexity due to postprandial changes in the gastrointestinal (GI) tract. A system that allows for the assessment of the multivariate interplay occurring following administration of an oral dose, in the presence or absence of meal, would greatly benefit the early stages of drug development. This is particularly true in an era when the majority of new molecular entities are highly permeable, poorly soluble, extensively metabolized compounds (BDDCS Class 2), which present the most complicated relationship in defining the impact of transporters due to the marked effects of transporter-enzyme interplay. This review evaluates the GI luminal environment by taking into account the absorption/transport/elimination interplay and evaluates the physiochemical property issues by taking into account the importance of solubility, permeability and metabolism. We concentrate on the BDDCS and its utility in predicting drug disposition. Furthermore, we focus on the effect of food on the extent of drug availability (F), which appears to follow closely what might be expected if a significant effect of high fat meals is inhibition of transporters. That is, high fat meals and lipidic excipients would be expected to have little effect on F for Class 1 drugs; they would increase F of Class 2 drugs, while decreasing F for Class 3 drugs.

Mechanistic Approaches to Volume of Distribution Predictions: Understanding the Processes

PURPOSE

To use recently developed mechanistic equations to predict tissue-to-plasma water partition coefficients (Kpus), apply these predictions to whole body unbound volume of distribution at steady state (Vu(ss)) determinations, and explain the differences in the extent of drug distribution both within and across the various compound classes.

MATERIALS AND METHODS

Vu(ss) values were predicted for 92 structurally diverse compounds in rats and 140 in humans by two approaches. The first approach incorporated Kpu values predicted for 13 tissues whereas the second was restricted to muscle.

RESULTS

The prediction accuracy was good for both approaches in rats and humans, with 64-78% and 82-92% of the predicted Vu(ss) values agreeing with in vivo data to within factors of +/-2 and 3, respectively.

CONCLUSIONS

Generic distribution processes were identified as lipid partitioning and dissolution where the former is higher for lipophilic unionised drugs. In addition, electrostatic interactions with acidic phospholipids can predominate for ionised bases when affinities (reflected by binding to constituents within blood) are high. For acidic drugs albumin binding dominates when plasma protein binding is high. This ability to explain drug distribution and link it to physicochemical properties can help guide the compound selection process.

Isostructural Re and 99mTc Complexes of Biotin Derivatives for Fluorescence and Radioimaging Studies

The reaction of biotinamine with two equivalents of 2-quinoline aldehyde in the presence of Na(OAc)3BH in dichloroethane provides N,N-bis(methylquinoline)biotinamine (L1), a molecule displaying a tridentate donor terminus which has proven effective in coordinating to the {M(CO)3}+ core (M = Tc, Re). Reaction of L1 with (NEt4)2[Re(CO)3Br3] yields [Re(CO)3(L1)]Br, a compound with an absorbance at 350 nm and luminescence emission maxima at 425 and 580 nm. The luminescence lifetime of 11.4 mus, which is associated with the 580 nm emission, is sufficiently prolonged to enable time-gating techniques to be used during in vitro imaging studies and to overcome interference from endogenous fluorescence. Exposure of avidin beads to {Re(CO)3(L1)]Br resulted in binding, which was qualitatively imaged using fluorescence microscopy. The 99mTc analogue [99mTc(CO)3(L1)]+1 was prepared by reacting L1 with [99mTc(CO)3(H2O)3]+1 and purified by HPLC. The 99mTc complex is chemically robust and resistant to cysteine and histidine challenges. This study demonstrates that complementary fluorescent and radioactive biotin-derived probes may be readily prepared to allow direct correlation of in vitro and in vivo molecular imaging studies.