Patrick Poulin

A priori prediction of tissue:plasma partition coefficients of drugs to facilitate the use of physiologically-based pharmacokinetic models in drug discovery

The tissue:plasma (P(t:p)) partition coefficients (PCs) are important drug-specific input parameters in physiologically based pharmacokinetic (PBPK) models used to estimate the disposition of drugs in biota. Until now the use of PBPK models in early stages of the drug discovery process was not possible, since the estimation of P(t:p) of new drug candidates by using conventional in vitro and/or in vivo methods is too time and cost intensive. The objectives of the study were (i) to develop and validate two mechanistic equations for predicting a priori the rabbit, rat and mouse P(t:p) of non-adipose and non-excretory tissues (bone, brain, heart, intestine, lung, muscle, skin, spleen) for 65 structurally unrelated drugs and (ii) to evaluate the adequacy of using P(t:p) of muscle as predictors for P(t:p) of other tissues. The first equation predicts P(t:p) at steady state, assuming a homogenous distribution and passive diffusion of drugs in tissues, from a ratio of solubility and macromolecular binding between tissues and plasma. The ratio of solubility was estimated from log vegetable oil:water PCs (K(vo:w)) of drugs and lipid and water levels in tissues and plasma, whereas the ratio of macromolecular binding for drugs was estimated from tissue interstitial fluid-to-plasma concentration ratios of albumin, globulins and lipoproteins. The second equation predicts P(t:p) of drugs residing predominantly in the interstitial space of tissues. Therefore, the fractional volume content of interstitial space in each tissue replaced drug solubilities in the first equation. Following the development of these equations, regression analyses between P(t:p) of muscle and those of the other tissues were examined. The average ratio of predicted-to-experimental P(t:p) values was 1.26 (SD = 1.40, r = 0.90, n = 269), and 85% of the 269 predicted values were within a factor of three of the corresponding literature values obtained under in vivo and in vitro conditions. For predicted and experimental P(t:p), linear relationships (r > 0.9 in most cases) were observed between muscle and other tissues, suggesting that P(t:p) of muscle is a good predictor for the P(t:p) of other tissues. The two previous equations could explain the mechanistic basis of these linear relationships. The practical aim of this study is a worthwhile goal for pharmacokinetic screening of new drug candidates.

Utility of physiologically based pharmacokinetic models to drug development and rational drug discovery candidate selection

The present paper proposes a modeling and simulation strategy for the prediction of pharmacokinetics (PK) of drug candidates by using currently available in silico and in vitro based prediction tools for absorption, distribution, metabolism and excretion (ADME). These methods can be used to estimate specific ADME parameters (such as rate and extent of absorption into portal vein, volume of distribution, metabolic clearance in the liver). They can also be part of a physiologically based pharmacokinetic (PBPK) model to simulate concentration-time profiles in tissues and plasma resulting from the overall PK after intravenous or oral administration. Since the ADME prediction tools are built only on commonly generated in silico and in vitro data, they can be applied already in early drug discovery, prior to any in vivo study. With the suggested methodology, the following advantages of the mechanistic PBPK modeling framework can now be utilized to explore potential clinical candidates already in drug discovery: (i) prediction of plasma (blood) and tissue PK of drug candidates prior to in vivo experiments, (ii) supporting a better mechanistic understanding of PK properties, as well as helping the development of more rationale PK-PD relationships from tissue kinetic data predicted, and hence facilitating a more rational decision during clinical candidate selection, and (iii) the extrapolation across species, routes of administration and dose levels.

Prediction of adipose tissue: Plasma partition coefficients for structurally unrelated drugs

Tissue:plasma (P(t:p)) partition coefficients (PCs) are important parameters describing tissue distribution of drugs. The ultimate goal in early drug discovery is to develop and validate in silico methods for predicting a priori the P(t:p) for each new drug candidate. In this context, tissue composition-based equations have recently been developed and validated for predicting a priori the non-adipose and adipose P(t:p) for neutral organic solvents and pollutants. For ionizable drugs that bind to different degrees to common plasma proteins, only their non-adipose P(t:p) values have been predicted with these equations. The only compound-dependent input parameters for these equations are the lipophilicity parameter, such as olive oil-water PC (K(vo:w)) or n-octanol-water PC (P(o:w)), and/or unbound fraction in plasma (fu(p)) determined under in vitro conditions. Tissue composition-based equations could potentially also be used to predict adipose tissue-plasma PCs (P(at:p)) for ionized drugs. The main objective of the present study was to modify these equations for predicting in vivo P(at:p) (white fat) for 14 structurally unrelated ionized drugs that bind substantially to plasma macromolecules in rats, rabbits, or humans. The second objective was to verify whether K(vo:w) or P(o:w) provides more accurate predictions of in vivo P(at:p) (i.e., to verify whether olive oil or n-octanol is the better surrogate for lipids in adipose tissue). The second objective was supported by comparing in vitro data on P(at:p) with those on olive oil-plasma PC (K(vo:p)) for five drugs. Furthermore, in vivo P(at:p) was not only predicted from K(vo:w) and P(o:w) of the non-ionized species, but also from K*(vo:w) and P*(o:w), taking into account the ionized species in addition. The P(at:p) predicted from K*(vo:w), P*(o:w), and P(o:w) differ from the in vivo P(at:p) by an average factor of 1.17 (SD = 0.44, r = 0.95), 15.0 (SD = 15.7, r = 0.59), and 40.7 (SD = 57.2, r = 0.33), respectively. The in vitro values of K(vo:p) differ from those of P(at:p) by an average factor of 0.86 (SD = 0.16, r = 0.99, n = 5). The results demonstrate that (i) the equation using only data on fu(p) as input and olive oil as lipophilicity surrogate is able to provide accurate predictions of in vivo P(at:p), and (ii) olive oil is a better surrogate of the adipose tissue lipids than n-octanol. The present study is an innovative method for predicting in vivo fat partitioning of drugs in mammals.

An algorithm for predicting tissue : Blood partition coefficients of organic chemicals from n‐octanol: Water partition coefficient data

The objectives of the present study were (1) to develop an algorithm to predict tissue:blood partition coefficients (PCs) of organic chemicals from n-octanol: water (Ko/w) PC data, and (2) to apply this algorithm to predict the rat tissue:blood PCs of some relatively hydrophilic organics, particularly ketones, alcohols, and acetate esters. The algorithm, developed by modifying a previously published one, involved predicting tissue:blood PCs of chemicals by dividing their partitioning into tissues by the sum of their partitioning into erythrocytes and plasma. The partitioning of a chemical into tissues, erythrocytes, and plasma was expressed as an additive function of its partitioning into neutral lipids, phospholipids, and water contained in them. The muscle, liver, and adipose tissue:blood PCs predicted with the present method were compared with the experimental values obtained from the literature for five ketones, eight alcohols, and eight acetate esters. The predicted muscle:blood and liver:blood PCs for the set of 21 hydrophilic organics were within a factor of 1.01 and 0.99 (on an average), respectively, of the experimental values. However, the predicted adipose tissue:blood PCs of the hydrophilic organics were greater than the experimental values by a factor of 4.13, which improved when vegetable oil:saline (Ko/s) PCs were used instead of Ko/w PCs (factor of 1.51). Overall, the use of the present algorithm should enable the prediction of tissue:blood PCs for organic chemicals for which Ko/w or Ko/s data are available.

PHRMA CPCDC initiative on predictive models of human pharmacokinetics, part 5: Prediction of plasma concentration–time profiles in human by using the physiologically‐based pharmacokinetic modeling approach

The objective of this study is to assess the effectiveness of physiologically based pharmacokinetic (PBPK) models for simulating human plasma concentration-time profiles for the unique drug dataset of blinded data that has been assembled as part of a Pharmaceutical Research and Manufacturers of America initiative. Combinations of absorption, distribution, and clearance models were tested with a PBPK approach that has been developed from published equations. An assessment of the quality of the model predictions was made on the basis of the shape of the plasma time courses and related parameters. Up to 69% of the simulations of plasma time courses made in human demonstrated a medium to high degree of accuracy for intravenous pharmacokinetics, whereas this number decreased to 23% after oral administration based on the selected criteria. The simulations resulted in a general underestimation of drug exposure (Cmax and AUC0- t ). The explanations for this underestimation are diverse. Therefore, in general it may be due to underprediction of absorption parameters and/or overprediction of distribution or oral first-pass. The implications of compound properties are demonstrated. The PBPK approach based on in vitro-input data was as accurate as the approach based on in vivo data. Overall, the scientific benefit of this modeling study was to obtain more extensive characterization of predictions of human PK from PBPK methods.

PhRMA CPCDC initiative on predictive models of human pharmacokinetics, part 3: Comparative assessement of prediction methods of human clearance

The objective of this study was to evaluate the performance of various allometric and in vitro-in vivo extrapolation (IVIVE) methodologies with and without plasma protein binding corrections for the prediction of human intravenous (i.v.) clearance (CL). The objective was also to evaluate the IVIVE prediction methods with animal data. Methodologies were selected from the literature. Pharmaceutical Research and Manufacturers of America member companies contributed blinded datasets from preclinical and clinical studies for 108 compounds, among which 19 drugs had i.v. clinical pharmacokinetics data and were used in the analysis. In vivo and in vitro preclinical data were used to predict CL by 29 different methods. For many compounds, in vivo data from only two species (generally rat and dog) were available and/or the required in vitro data were missing, which meant some methods could not be properly evaluated. In addition, 66 methods of predicting oral (p.o.) area under the curve (AUCp.o. ) were evaluated for 107 compounds using rational combinations of i.v. CL and bioavailability (F), and direct scaling of observed p.o. CL from preclinical species. Various statistical and outlier techniques were employed to assess the predictability of each method. Across methods, the maximum success rate in predicting human CL for the 19 drugs was 100%, 94%, and 78% of the compounds with predictions falling within 10-fold, threefold, and twofold error, respectively, of the observed CL. In general, in vivo methods performed slightly better than IVIVE methods (at least in terms of measures of correlation and global concordance), with the fu intercept method and two-species-based allometry (rat-dog) being the best performing methods. IVIVE methods using microsomes (incorporating both plasma and microsomal binding) and hepatocytes (not incorporating binding) resulted in 75% and 78%, respectively, of the predictions falling within twofold error. IVIVE methods using other combinations of binding assumptions were much less accurate. The results for prediction of AUCp.o. were consistent with i.v. CL. However, the greatest challenge to successful prediction of human p.o. CL is the estimate of F in human. Overall, the results of this initiative confirmed predictive performance of common methodologies used to predict human CL.

A biologically-based algorithm for predicting human tissue: blood partition coefficients of organic chemicals

A biologically-based algorithm for predicting the tissue: blood partition coefficients (PCs) of organic chemicals has been developed. The approach consisted of (i) describing tissues and blood in terms of their neutral lipid, phospho lipid, and water contents, (ii) obtaining data on the solu bility of chemicals in n-octanol and water, and (iii) calcu lating the tissue: blood PCs by assuming that the solubility of a chemical in n-octanol corresponds to its solubility in neutral lipids, the solubility in water corresponds to the solubility in tissue/blood water fraction, and the solubility in phospholipids is a function of solubility in water and n- octanol. The adequacy of this approach was verified by compar ing the predicted values with previously published experi mental data on human tissue (liver, lung, muscle, kidney, brain, adipose tissue): blood PCs for 23 organic chemicals. In the case of liver, lung, and muscle, the predicted PC val ues were in close agreement with the higher-end of the range of experimental PC values found in the literature. The predicted brain: and kidney: blood PCs were greater than the experimental PCs in most cases by approximately a factor of two. Whereas the adipose tissue: blood PCs of relatively less hydrophilic chemicals were adequately pre dicted, the predicted PCs for relatively more hydrophilic chemicals were much greater than the experimentally- determined values. There was a good agreement between the predicted and experimentally-determined blood solubility of the 23 chemicals chosen for this study, indicating that the over- estimation of tissue:blood PCs by the present method is not due to under-estimation of blood solubility of chemicals. Rather, it might be due to the lower tissue solubility of chemicals observed experimentally due to the complexity of the tissue matrices. This novel approach of describing tissues in terms of the type of lipid and water content should enable the predic tion of the tissue:blood PCs of organic chemicals with information on their solubility in water and n-octanol, for developing physiologically-based toxicokinetic models.

Relative lipid content as the sole mechanistic determinant of the adipose tissue:blood partition coefficients of highly lipophilic organic chemicals

The adipose tissue:blood partition coefficient (PCat:b) refers to the ratio of chemical concentration or solubility in adipose tissue and blood. The solubility of a chemical in adipose tissue or whole blood is equal to the sum total of its solubility in lipid and water fractions of these matrices. For highly lipophilic organic chemicals (HLOCs, i.e., chemicals with log n-octanol:water partition coefficients (PCo:w) greater than four), their solubility in the water fractions of both tissue and blood is negligible, and therefore their solubility in lipid fractions of tissue and blood alone determines PCat:b. Since the numerical value representing chemical solubility in lipids is likely to be the same for both blood lipids and adipose tissue lipids, the PCat:b values should be hypothetically, equal to the ratio of lipid content of adipose tissue and blood. The objective of the present study was therefore to verify whether the PCat:bs of HLOCs (volatile organics, dioxins, PCBs, PBBs, DDT) are equal to the ratio of adipose tissue and blood lipid levels. The data on lipid content of rat and human blood and adipose tissues were obtained from the literature. The calculated tissue:blood lipid ratios were comparable to the human and rat PCat:b of volatile organic chemicals, dioxins, PCBs, PBBs and/or DDT obtained from the literature. These results then suggest that, regardless of the identity and PCo:w of HLOCs, their PCat:b is equal to the ratio of lipid in adipose tissues and blood.

A unified algorithm for predicting partition coefficients for PBPK modeling of drugs and environmental chemicals

The algorithms in the literature focusing to predict tissue:blood PC (P(tb)) for environmental chemicals and tissue:plasma PC based on total (K(p)) or unbound concentration (K(pu)) for drugs differ in their consideration of binding to hemoglobin, plasma proteins and charged phospholipids. The objective of the present study was to develop a unified algorithm such that P(tb), K(p) and K(pu) for both drugs and environmental chemicals could be predicted. The development of the unified algorithm was accomplished by integrating all mechanistic algorithms previously published to compute the PCs. Furthermore, the algorithm was structured in such a way as to facilitate predictions of the distribution of organic compounds at the macro (i.e. whole tissue) and micro (i.e. cells and fluids) levels. The resulting unified algorithm was applied to compute the rat P(tb), K(p) or K(pu) of muscle (n=174), liver (n=139) and adipose tissue (n=141) for acidic, neutral, zwitterionic and basic drugs as well as ketones, acetate esters, alcohols, aliphatic hydrocarbons, aromatic hydrocarbons and ethers. The unified algorithm reproduced adequately the values predicted previously by the published algorithms for a total of 142 drugs and chemicals. The sensitivity analysis demonstrated the relative importance of the various compound properties reflective of specific mechanistic determinants relevant to prediction of PC values of drugs and environmental chemicals. Overall, the present unified algorithm uniquely facilitates the computation of macro and micro level PCs for developing organ and cellular-level PBPK models for both chemicals and drugs.

Development of a novel method for predicting human volume of distribution at steady-state of basic drugs and comparative assessment with existing methods.

The parameters characterizing tissue distribution refer to the tissue/plasma partition coefficients (Kp), which can be used to derive volume of distribution at steady-state (V(ss)). The effort for predicting drug distribution in human has been further expanded to calculation methods using in vitro-based algorithms. The objective of the present study was to develop a novel prediction method to estimate human V(ss) for moderate-to-strong bases. The predictive performance of the novel method was compared with other well established in vitro-based methods available in the literature. Relevant information collected from previous prediction studies of V(ss) facilitated the development of the novel method. This was based on the calculation of V(ss) from data on Kp, which were estimated by correlating the unbound tissue/plasma ratio in vivo (Kpu) with the unbound red blood cells partitioning (RBCu) determined in vitro. The comparative assessment of the novel correlation method with existing prediction methods of human V(ss) was done using a literature dataset of 61 basic drugs (at least one pK(a) > or = 7). The five existing V(ss) prediction methods published in the literature are comprised of four versions of tissue composition-based models along with the model of Lombardo using the principle of Oie-Tozer. The statistical analysis of the prediction performance indicated that the novel method demonstrated a greater degree of accuracy compared to all other published methods. The maximum percentage of predicted values that fall within a twofold-error range is 77% for the basic drugs tested. Overall, the present study describes the development and the assessment of the predictive performance of the novel prediction method of human V(ss) based upon in vitro data, which appears to be superior based on the current dataset studied for basic drugs.