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The gastrointestinal uptake of macrocyclic compounds is not fully understood. Here we applied our previously validated integrated system based on machine learning and conformal prediction to predict the passive fraction absorbed (f

Variability of the unbound fraction in plasma (f

Well-established techniques are available to predict in vivo hepatic uptake and metabolism from in vitro data, but predictive models for biliary clearance remain elusive. Several studies have verified the expression and activity of ATP-binding cassette (ABC) efflux transporters central to biliary clearance in freshly isolated rat hepatocytes, raising the possibility of predicting biliary clearance from in vitro efflux measurements. In the present study, short-term plated rat hepatocytes were evaluated as a model to predict biliary clearance from in vitro efflux measurements before major changes in transporter expression known to take place in long-term hepatocyte cultures. The short-term cultures were carefully characterized for their uptake and metabolic properties using a set of model compounds. In vitro efflux was studied using digoxin, fexofenadine, napsagatran, and rosuvastatin, representing compounds with over 100-fold differences in efflux rates in vitro and 60-fold difference in measured in vivo biliary clearance. The predicted biliary clearances from short-term plated rat hepatocytes were within 2-fold of measured in vivo values. As in vitro efflux includes both basolateral and canalicular effluxes, pronounced basolateral efflux may introduce errors in predictions for some compounds. In addition, in vitro rat hepatocyte uptake rates corrected for simultaneous efflux predicted rat in vivo hepatic clearance of the biliary cleared compounds with less than 2-fold error. Short-term plated hepatocytes could thus be used to quantify hepatocyte uptake, metabolism, and efflux of compounds and considerably improve the prediction of hepatic clearance, especially for compounds with a large biliary clearance component.

The blood-brain barrier is often perceived as relatively impermeable, and various cut-off values for zero or limited brain permeability have been suggested. The validity of these values has been evaluated in this review. The barrier appears to have a very high permeability and absorptive capacity: sufficient to absorb compounds with polar surface area270 A(2), molecular weight1,000 Da, log D-3.5 and equilibrium brain-to-blood concentration ratio0.01 well. Sufficient intestinal uptake indicates good passive brain uptake potential. The uptake is potentially more sensitive to involvement and changes of active transport than in the intestines. A physiologically based in vitro-in vivo method for prediction of brain uptake is presented.

Permeability (Pe) and solubility/dissolution are two major determinants of gastrointestinal (GI) drug absorption. Good prediction of these is crucial for predicting doses, exposures and potential interactions, and for selecting appropriate candidate drugs. The main objective was to evaluate screening methods for prediction of GI Pe, solubility/dissolution and fraction absorbed (fa) in humans. The most accurate Pe models for prediction of fa of passively transported and highly soluble compounds appear to be the 2/4/A1 rat small intestinal cell model (in-vitro and in-silico), a newly developed artificial-membrane method, and a semi-empirical approach based on in-vitro membrane affinity to immobilized lipid bilayers, effective molecular weight and physiological GI variables. The predictability of in-vitro Caco-2, in-situ perfusion and other artificial membrane methods seems comparably low. The Pe and fa in humans for compounds that undergo mainly active transport were predicted poorly by all models investigated. However, the rat in-situ perfusion model appears useful for prediction of active uptake potential (complete active uptake is generally well predicted), and Caco-2 cells are useful for studying bidirectional active transport, respectively. Human intestinal in-vitro Pe, which correlates well with fa for passively transported compounds, could possibly also have potential to improve/enable predictions of fa for actively transported substances. Molecular descriptor data could give an indication of the passive absorption potential. The ‘maximum absorbable dose’ and ‘dose number’ approaches, and solubility/dissolution data obtained in aqueous media, appear to underestimate in-vivo dissolution to a considerable extent. Predictions of in-vivo dissolution should preferably be done from in-vitro dissolution data obtained using either real or validated simulated GI fluids.

The goal of this study was to determine the most appropriate hydrodynamic model for the Loc-I-Gut in-vivo perfusion system. The general mixing-tank-in-series model, which can approximate single mixing tank and laminar and plug-flow hydrodynamics, was fitted to the observed experimental residence-time distribution curves for the non-absorbable marker [14C]PEG 4000. The residence-time distribution analysis shows that the hydrodynamics of the perfusion solution within the jejunal segment in man is well approximated by a model containing on average between 1–2 mixing tanks in series. The solution is well mixed when using perfusion rates of 20, 30 and 60 mL min−. The average mean residence time estimates from the fitted residence-time distribution were 12 ± 7.6, 15 ± 4.2 and 7.7 ± 4.6 min, respectively, at these three perfusion rates. The mean volumes of the segment (Vs) were 25 ± 15, 45 ± 12 and 46 ± 27 mL, respectively. There were no statistical differences between 20, 30 and 60 mL min− in respect of the number of mixing tanks (n) and mean residence times. This residence-time distribution analysis indicates that the luminal fluid in the Loc-I-Gut perfusion system is well-mixed, and that permeability calculations based on the well-mixed assumption most closely approximate the actual local (average) membrane permeability within the perfused segment.

The main objectives were to investigate the roles of and interplay between determinants of hepatic clearance (CL(H)) in humans, to develop a methodology and reference system for the evaluation and prediction of the rate-limiting step in CL(H), and to update the dispersion model and compare it with traditionally used liver extraction models. The new methodology enables predictions of the hepatic uptake and CL(H), dissociation, and rate-limiting step. In general, absorption, dissociation and diffusion are comparably rapid processes, and metabolism is rate-limiting. The liver appears to have a high passive uptake capacity. The Modified Dispersion Model (MDM) has a dispersion number of 0.5 and a distribution factor (df = 0.87) for the correction of a longer hepatic transit time of unbound molecules and the exclusion of the hidden unbound fraction within erythrocytes. Liver models are functionally equivalent at low CL(H), but differ for highly extracted compounds. Well-stirred and parallel-tube models demonstrate the greatest difference in performance, for example, 6- and 800,000-fold differences in the estimated in vivo intrinsic CL(H) and predicted oral bioavailability of the high CL(H) drug naloxone, respectively. The roles of and interplay between determinants of CL(H) have been further clarified and can now be better predicted. Apparent advantages with the MDM include its scientific rationale and intermediate/ balanced performance.

The kidneys have the capability to both excrete and metabolise drugs. An understanding of mechanisms that determine these processes is required for the prediction of pharmacokinetics, exposures, doses and interactions of candidate drugs. This is particularly important for compounds predicted to have low or negligible non-renal clearance (CL). Clinically significant interactions in drug transport occur mostly in the kidneys. The main objective was to evaluate methods for prediction of excretion and metabolic renal CL (CLR) in humans. CLR is difficult to predict because of the involvement of bi-directional passive and active tubular transport, differences in uptake capacity, pH and residence time on luminal and blood sides of tubular cells, and limited knowledge about regional tubular residence time, permeability (Pe) and metabolic capacity. Allometry provides poor predictions of excretion CLR because of species differences in unbound fraction, urine pH and active transport. The correlation between fraction excreted unchanged in urine (fe) in humans and animals is also poor, except for compounds with high passive Pe (extensive/complete tubular reabsorption; zero/negligible fe) and/or high non-renal CL. Physiologically based in-vitro/in-vivo methods could potentially be useful for predicting CLR. Filtration could easily be predicted. Prediction of tubular secretion CL requires an in-vitro transport model and establishment of an in-vitro/in-vivo relationship, and does not appear to have been attempted. The relationship between passive Pe and tubular fraction reabsorbed (freabs) for compounds with and without apparent secretion has recently been established and useful equations and limits for prediction were developed. The suggestion that reabsorption has a lipophilicity cut-off does not seem to hold. Instead, compounds with passive Pe that is less than or equal to that of atenolol are expected to have negligible passive freabs. Compounds with passive Pe that is equal to or higher than that of carbamazepine are expected to have complete freabs. For compounds with intermediate Pe the relationship is irregular and freabs is difficult to predict. Tubular cells are comparably impermeable (for passive diffusion), and show regional differences in enzymatic and transporter activities. This limits the usefulness of microsome data and makes microsome-based predictions of metabolic CLR questionable. Renal concentrations and activities of CYP450s are comparably low, suggesting that CYP450 substrates have negligible metabolic CLR. The metabolic CLR of high-Pe UDP-glucuronyltransferase substrates could contribute to the total CL.

Intestinal mucosal cells operate with different metabolic and transport activity, and not all of them are involved in drug absorption and metabolism. The fraction of these cells involved is dependent on the absorption characteristics of compounds and is difficult to predict (it is probably small). The cells also appear comparably impermeable. This shows a limited applicability of microsome intrinsic clearance (CLint)-data for prediction of gut-wall metabolism, and the difficulty to predict the gut-wall CL (CLGW) and extraction ratio (EGW). The objectives of this review were to evaluate determinants and methods for prediction of first-pass and systemic EGW and CLGW in man, and if required and possible, develop new simple prediction methodology. Animal gut-wall metabolism data do not appear reliable for scaling to man. In general, the systemic CLGW is low compared with the hepatic CL. For a moderately extracted CYP3A4-substrate with high permeability, midazolam, the gut-wall/hepatic CL-ratio is only 1/35. This suggests (as a general rule) that systemic CLGW can be neglected when predicting the total CL. First-pass EGW could be of importance, especially for substrates of CYP3A4 and conjugating enzymes. For several reasons, including those presented above and that blood flow based models are not applicable in the absorptive direction, it seems poorly predicted with available methodology. Prediction errors are large (several-fold on average; maximum-15-fold). A new simple first-pass EGW-prediction method that compensates for regional and local differences in absorption and metabolic activity has been developed. It has been based on human cell in-vitro CLint and fractional absorption from the small intestine for reference (including verapamil) and test substances, and in-vivo first-pass EGW-data for reference substances. First-pass EGW-values for CYP3A4-substrates with various degrees of gastrointestinal uptake and CLint and a CYP2D6-substrate were well-predicted (negligible errors). More high quality in-vitro CLint- and in-vivo EGW-data are required for further validation of the method.

Physiologically based methods generally perform poorly in predicting in-vivo hepatic CL (CLH) from intrinsic clearance (CLint) in microsomes in-vitro and unbound fraction in blood (fu,bl). Various strategies to improve the predictability have been developed, and inclusion of an empirical scaling factor (SF) seems to give the best results. This investigation was undertaken to evaluate this methodology and to find ways to improve it further. The work was based on a diverse data set taken from Ito and Houston (2005). Another objective was to evaluate whether rationalization of CLH predictions can be made by replacing blood/plasma-concentration ratio (Cbl/Cpl) measurements with SFs. There were apparently no or weak correlations between prediction errors and lipophilicity, permeability (compounds with low permeability missing in the data set) and main metabolizing CYP450s. The use of CLint class (high/low) and drug class (acid/base/neutral) SFs (the CD-SF method) gives improved and reasonable predictions: 1.3-fold median error (an accurate prediction has a 1-fold error), 76% within 2-fold-error, and a median absolute rank ordering error of 2 for CLH (n = 29). This approach is better than the method with a single SF. Mean (P < 0.05) and median errors, fraction within certain error ranges, higher percentage with most accurate predictions, and ranking were all better, and 76% of predictions were more accurate with this new method. Results are particularly good for bases, which generally have higher CLH and the potential to be incorrectly selected/rejected as candidate drugs. Reasonable predictions of fu,bl can be made from plasma fu (fu,pl) and empirical blood cell binding SFs (B-SFs; 1 for low fu,pl acids; 0.62 for other substances). Mean and median fu,bl prediction errors are negligible. The use of the CD-SF method with predicted fu,bl (the BCD-SF method) also gives improved and reasonable results (1.4-fold median error; 66% within 2-fold-error; median absolute rank ordering error = 1). This new empirical approach seems sufficiently good for use during the early screening; it gives reasonable estimates of CLH and good ranking, which allows replacement of Cbl/Cpl measurements by a simple equation.

This review has evaluated the Biopharmaceutics Classification System (BCS) and improvements have been proposed. The BCS has a very strict solubility/dissolution limit, a generous Pe-limit (≥ 14-times higher rate constant limit for dissolution than for permeation), and is stricter for drugs with a long half-life (t1/2). Available human in-vivo, in-vitro, and in-silico Pe-methods cannot classify Pe for moderately to highly permeable substances sufficiently well, and in-vitro data often underpredict the in-vivo dissolution potential and rate. Good in-vivo dissolution and absorption can be expected for most high Pe drug products. It has not been possible to find a highly permeable product with a Dose number (Do) < 385 (< 2400 in the fed state) that is clearly incompletely absorbed, and near complete uptake has been shown for a drug product with a Do of 660000. The potential implication of these findings is that many true BCS Class I drug products are incorrectly classified. This could be a reason for the limited use of this system. On this basis, it has been suggested that: the limit for high for solubility/dissolution is decreased (to >40 and >95% dissolved within 30min and 3 h, respectively); the limit for high Pe is increased (to >Pe of metoprolol); accurate Pe-models or in-vivo fraction absorbed data are used; solubility/dissolution tests are performed using real or validated simulated gastrointestinal fluids; in-vitro/in-vivo dissolution relationships are established; the t½ is considered; and the rate-limiting step for in-vivo absorption is determined. A major change could be to reduce the BCS into two classes: permeation-rate (Class I) or dissolution-rate (Class II) limited absorption. It is believed that this could give a better balance and increase the number of biowaivers.

The objective was to establish in vitro passive permeability (P e) vs in vivo fraction absorbed (f a)-relationships for each passage through the human intestine, liver, renal tubuli and brain, and develop a P e-based ADME/PK classification system (PCS). P e- and intestinal f a-data were taken from an available data set. Hepatic f a was calculated based on extraction ratios of the unbound fraction of drugs (with support from animal in vivo uptake data). Renal f a (reabsorption) was estimated using renal pharmacokinetic data, and brain f a was predicted using animal in vitro and in vivo brain P e-data. Hepatic and intestinal f a-data were used to predict bile excretion potential. Relationships were established, including predicted curves for bile excretion potential and minimum oral bioavailability, and a 4-Class PCS was developed: I (very high P e; elimination mainly by metabolism); II (high P e) and III (intermediate P e and incomplete f a); IV (low P e and f a). The system enables assessment of potential drug–drug transport interactions, and drug and metabolite organ trapping. The PCS and high quality P e-data (with and without active transport) are believed to be useful for predictions and understanding of ADME/PK, elimination routes, and potential interactions and organ trapping/toxicity in humans.