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Oral bioavailability (F) is an essential determinant for the systemic exposure and dosing regimens of drug candidates. F is determined by numerous processes, and computational predictions of human estimates have so far shown limited results. We describe a new methodology where F in humans is predicted directly from chemical structure using an integrated strategy combining 9 machine learning models, 3 sets of structural alerts, and 2 physiologically-based pharmacokinetic models. We evaluate the model on a benchmark dataset consisting of 184 compounds, obtaining a predictive accuracy (Q2) of 0.50, which is successful according to a pharmaceutical industry proposal. Twenty-seven compounds were found (beforehand) to be outside the main applicability domain for the model. We compare our results with interspecies correlations (rat, mouse and dog vs. human) using the same dataset, where animal vs. human-correlations (R2) were found to be 0.21 to 0.40 and maximum prediction errors were smaller than maximum interspecies differences. We conclude that our method has sufficient predictive accuracy to be practically useful with applications in human exposure and dose predictions, compound optimization and decision making, with potential to rationalize drug discovery and development and decrease failures and overexposures in early clinical trials with candidate drugs.

The main objective was to evaluate and propose methods for predicting biliary clearance (CLbile) and enterohepatic circulation (EHC) of intact drugs in man. Another aim was to evaluate to role of intestinal drug secretion and propose a method for prediction of intestinal secretion CL (CLi). Animal data poorly predict the CL and CLbile of biliary excreted drugs, and the suggested molecular weight threshold for bile excretion as the dominant elimination route does not seem to hold. Active transport, low metabolic intrinsic CL (CLint) and, as an approximation, permeability (Pe) less than that of metoprolol is required for substantial CLbile to occur. The typical EHC plasma concentration vs time profile (multiple peaks) is demonstrated for many low metabolic CLint-compounds with efflux and moderate to high intestinal Pe and fraction absorbed. Physiologically-based in-vitro to in-vivo (PB-IVIV) methodology with in-vitro intrinsic CLbile-data obtained with sandwich-cultured human hepatocytes has generated 2- and 5-fold underpredictions for two compounds with intermediate to high CLbile. This is despite not considering the unbound fraction. Possible explanations include low transporter activity and diffusion limitations in the in-vitro experiments. Intestinal reabsorption and EHC were also neglected in these predictions and in-vivo CLbile estimations. The sandwich model and these reference data are still very useful. Consideration of an empirical scaling factor and a newly developed approach that accounts for intestinal reabsorption and EHC could potentially lead to improved PB-IVIV predictions of CLbile. Apparently, no attempts have been made to predict CLi. Elimination via the intestinal route does not appear to be of great importance for the few compounds with available data, but could be equally as important as bile excretion. Net secretion in-vitro Pe and newly estimated in-vivo intrinsic CLi data for digoxin and rosuvastatin could be useful for approximation of CLi of other compounds. [ABSTRACT FROM AUTHOR]

Abstract Purpose  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). Materials and Methods   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. Results  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. Conclusions  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. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Methods for prediction of hepatic clearance (CLH) in man have been evaluated. A physiologically-based in-vitro to in-vivo (PB-IVIV) method with human unbound fraction in blood (fu, bl) and hepatocyte intrinsic clearance (CLint)-data has a good rationale and appears to give the best predictions (maximum ∼2-fold errors; < 25% errors for half of CL-predictions; appropriate ranking). Inclusion of an empirical scaling factor is, however, needed, and reasons include the use of cryo-preserved hepatocytes with low activity, and inappropriate CLint- and fu, bl-estimation methods. Thus, an improvement of this methodology is possible and required. Neglect of fu, bl or incorporation of incubation binding does not seem appropriate. When microsome CLint-data are used with this approach, the CLH is underpredicted by 5- to 9-fold on average, and a 106-fold underprediction (attrition potential) has been observed. The poor performance could probably be related to permeation, binding and low metabolic activity. Inclusion of scaling factors and neglect of fu, bl for basic and neutral compounds improve microsome predictions. The performance is, however, still not satisfactory. Allometry incorrectly assumes that the determinants for CLH relate to body weight and overpredicts human liver blood flow rate. Consequently, allometric methods have poor predictability. Simple allometry has an average overprediction potential, > 2-fold errors for ∼1/3 of predictions, and 140-fold underprediction to 5800-fold overprediction (potential safety risk) range. In-silico methodologies are available, but these need further development. Acceptable prediction errors for compounds with low and high CLH should be ∼50 and ∼10%, respectively. In conclusion, it is recommended that PB-IVIV with human hepatocyte CLint and fu, bl is applied and improved, limits for acceptable errors are decreased, and that animal CLH-studies and allometry are avoided. [ABSTRACT FROM AUTHOR]

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 (t½). 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 30 min 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. [ABSTRACT FROM AUTHOR]