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Optimizing the pharmacokinetics (PK) of a drug candidate to support oral dosing is a key challenge in drug development. PK parameters are usually estimated from the concentration–time profile following intravenous administration; however, traditional methods are time-consuming and expensive. In recent years, quantitative structure–pharmacokinetic relationship (QSPKR), an in silico tool that aims to develop a mathematical relationship between the structure of a molecule and its PK properties, has emerged as a useful alternative to experimental testing. Due to the complex nature of the various processes involved in dictating the fate of a drug, the development of adequate QSPKR models that can be used in real-world pre-screening situations has proved challenging. Given the crucial role played by a molecule's ionization state in determining its PK properties, this work aims to build predictive QSPKR models for PK parameters in humans using an ionization state-based strategy. We divide a high-quality dataset into clusters based on ionization state at physiological pH and build global and ion subset-based 'local' models for three major PK parameters: plasma clearance (CL), steady-state volume of distribution (VDss), and half-life (t1/2). We use a robust methodology developed in our lab entitled 'EigenValue ANalySis' that accounts for the stereospecificity in drug disposition and use the support vector machine algorithm for model building. Our findings suggest that categorizing compounds in accordance with ionization state does not result in improved QSPKR models. The narrow ranges in the endpoints along with redundancies in the data adversely affect the ion subset-based QSPKR models. We suggest alternative approaches such as elimination route-based models that account for drug–transporter interactions for CL and chemotype-specific QSPKR for VDss. [ABSTRACT FROM AUTHOR]

In the past, our lab proposed a two-pore PBPK model for different-size protein therapeutics using de novo derived parameters and the model was validated using plasma PK data of different-size antibody fragments digitized from the literature (Li Z, Shah DK, J Pharmacokinet Pharmacodynam 46(3):305–318, 2009). To further validate the model using tissue distribution data, whole-body biodistribution study of 6 different-size proteins in mice were conducted. Studied molecules covered a wide MW range (13–150 kDa). Plasma PK and tissue distribution profiles is 9 tissues were measured, including heart, lung, liver, spleen, kidney, skin, muscle, small intestine, large intestine. Tumor exposure of different-size proteins were also evaluated. The PBPK model was validated by comparing percentage predictive errors (%PE) between observed and model predicted results for each type of molecule in each tissue. Model validation showed that the two-pore PBPK model was able to predict plasma, tissues and tumor PK of all studied molecules relatively well. This model could serve as a platform for developing a generic PBPK model for protein therapeutics in the future. [ABSTRACT FROM AUTHOR]

Two-pore PBPK models have been used for characterizing the PK of protein therapeutics since 1990s. However, widespread utilization of these models is hampered by the lack of a priori parameter values, which are typically estimated using the observed data. To overcome this hurdle, here we have presented the development of a two-pore PBPK model using de novo derived parameters. The PBPK model was validated using plasma PK data for different size proteins in mice. Using the "two pore theory" we were able to establish the relationship between protein size and key model parameters, such as: permeability-surface area product (PS), vascular reflection coefficient (σ), peclet number (Pe), and glomerular sieving coefficient (θ). The model accounted for size dependent changes in tissue extravasation and glomerular filtration. The model was able to a priori predict the PK of 8 different proteins: IgG (150 kDa), scFv-Fc (105 kDa), F(ab)2 (100 kDa, minibody (80 kDa), scFv2 (55 kDa), Fab (50 kDa), diabody (50 kDa), scFv (27 kDa), and nanobody (13 kDa). In addition, the model was able to provide unprecedented quantitative insight into the relative contribution of convective and diffusive pathway towards trans-capillary mass transportation of different size proteins. The two-pore PBPK model was also able to predict systemic clearance (CL) versus Molecular Weight relationship for different size proteins reasonably well. As such, the PBPK model proposed here represents a bottom-up systems PK model for protein therapeutics, which can serve as a generalized platform for the development of truly translational PBPK model for protein therapeutics. [ABSTRACT FROM AUTHOR]

Purpose: To establish a continuous relationship between the size of various antibody fragments and their systemic clearance (CL) in mice. Methods: Two different orthogonal approaches have been used to establish the relationship. First approach uses CL values estimated by non-compartmental analysis (NCA) to establish a correlation with protein size. The second approach simultaneously characterizes the PK data for all the proteins using a 2-compartment model to establish a relationship between protein size and pharmacokinetic (PK) parameters. Results: Simple mathematical functions (e.g. sigmoidal, power law) were able to characterize the CL vs. protein size relationship generated using the investigated proteins. The relationship established in mouse was used to predict rat, rabbit, monkey, and human relationships using allometric scaling. The predicted relationships were found to capture the available spares data from each species reasonably well. Conclusions: The CL vs. protein size relationship is important for establishing a robust quantitative structure-PK relationship (QSPKR) for protein therapeutics. The relationship presented here can help in a priori predicting plasma exposure of therapeutic proteins, and together with our previously established relationship between plasma and tissue concentrations of proteins, it can predict the tissue exposure of non-binding proteins simply based on molecular weight/radius and dose. [ABSTRACT FROM AUTHOR]

Background: Volume of distribution is an important pharmacokinetic property that indicates the extent of a drug's distribution in the body tissues. This paper addresses the problem of how to estimate the apparent volume of distribution at steady state (Vss) of chemical compounds in the human body using decision tree-based regression methods from the area of data mining (or machine learning). Hence, the pros and cons of several different types of decision tree-based regression methods have been discussed. The regression methods predict Vss using, as predictive features, both the compounds' molecular descriptors and the compounds' tissue:plasma partition coefficients (Kt:p) - often used in physiologically-based pharmacokinetics. Therefore, this work has assessed whether the data mining-based prediction of Vss can be made more accurate by using as input not only the compounds' molecular descriptors but also (a subset of) their predicted Kt:p values. Results: Comparison of the models that used only molecular descriptors, in particular, the Bagging decision tree (mean fold error of 2.33), with those employing predicted Kt:p values in addition to the molecular descriptors, such as the Bagging decision tree using adipose Kt:p (mean fold error of 2.29), indicated that the use of predicted Kt:p values as descriptors may be beneficial for accurate prediction of Vss using decision trees if prior feature selection is applied. Conclusions: Decision tree based models presented in this work have an accuracy that is reasonable and similar to the accuracy of reported Vss inter-species extrapolations in the literature. The estimation of Vss for new compounds in drug discovery will benefit from methods that are able to integrate large and varied sources of data and flexible non-linear data mining methods such as decision trees, which can produce interpretable models. [ABSTRACT FROM AUTHOR]

The objective of this investigation was to compare the observed biliary clearance (CLb) and % of dose excreted in the bile (PDb) of mitoxantrone with the predicted values obtained from quantitative structure pharmacokinetic relationship (QSPKR) models. Blood and bile samples were collected from bile duct cannulated rats after an intravenous bolus dose of 0.5 or 2 mg/kg mitoxantrone, and the concentrations were measured by HPLC. Mitoxantrone plasma concentrations exhibited a tri-exponential profile with systemic clearance of 118 ± 6.8 mL/min/kg. After dosing, 6.08 ± 2.32% and 5.69 ± 0.59% of the dose were excreted into bile in unchanged form after a 3-h collection. CLb was 7.20 ± 4.54 and 7.46 ± 0.62 mL/min/kg after the two doses. With the co-administration of 10 mg/kg GF-120918, a P-glycoprotein and BCRP inhibitor, PDb was reduced to 0.69 ± 0.07%, suggesting that BCRP or P-glycoprotein may play an important role in the biliary elimination of mitoxantrone. Using QSPKR models developed for the biliary excretion of cations/neutral compounds in rats, CLb and PDb of mitoxantrone were predicted as 5.18 mL/min/kg and 7.21%, respectively, suggesting that the models could be used to predict the biliary excretion of mitoxantrone. © 2009 Wiley-Liss, Inc. and the American Pharmacists Association J Pharm Sci 99: 2502–2510, 2010 [ABSTRACT FROM AUTHOR]

The aims were (1) to evaluate the molecular weight (MW) dependence of biliary excretion and (2) to develop quantitative structure–pharmacokinetic relationships (QSPKR) to predict biliary clearance (CLb) and percentage of administered dose excreted in bile as parent drug (PDb) in rats and humans. CLb and PDb data were collected from the literature for rats and humans. Receiver operating characteristic curve analysis was utilized to determine whether a MW threshold exists for PDb. Stepwise multiple linear regression (MLR) was used to derive QSPKR models. The predictive performance of the models was evaluated by internal validation using the leave-one-out method and external test groups. A MW threshold of 400 Da was determined for PDb for anions in rats, while 475 Da was the cutoff for anions in humans. MW thresholds were not present for cations or cations/neutral compounds in either rats or humans. The QSPKR model for human CLb showed a significant correlation ( R 2 = 0.819) with good prediction performance ( Q 2 = 0.722). The model was further assessed using a test group, yielding a geometric mean fold-error of 2.68. QSPKR models with significant correlation and good predictability were also developed for CLb in rats and PDb data for anions or cation/neutral compounds in rats and humans. Both CLb and PDb data were further evaluated for subsets of MRP2 or P-glycoprotein substrates, and significant relationships were derived. QSPKR models were successfully developed for biliary excretion of non-congeneric compounds in rats and humans, providing a quantitative prediction of biliary clearance of compounds. [ABSTRACT FROM AUTHOR]

Metabolic drug-drug interactions are receiving more and more attention from the in silico community. Early prediction of such interactions would not only improve drug safety but also contribute to make drug design more predictable and rational. The aim of this study was to build a simple and interpretable model for the determination of the P450 enzyme predominantly responsible for a drug's metabolism. The P450 enzymes taken into consideration were CYP3A4, CYP2D6 and CYP2C9. Physico-chemical descriptors and structural descriptors for 96 currently marketed drugs were submitted to statistical analysis using the formal inference-based recursive modelling (FIRM) method, a form of recursive partitioning. Generally accepted knowledge on metabolism by these enzymes was also used to construct a hierarchical decision tree. Robust methods of variable selection using recursive partitioning were utilised. The descriptive ability of the resulting hierarchical model is very satisfactory, with 94% of the compounds correctly classified. [ABSTRACT FROM AUTHOR]

Simple methods for determining the human pharmacokinetics of known and unknown drug-like compounds is a much sought-after goal in the pharmaceutical industry. The current study made use of artificial neural networks (ANNs) for the prediction of clearances, fraction bound to plasma proteins, and volume of distribution of a series of structurally diverse compounds. A number of theoretical descriptors were generated from the drug structures and both automated and manual pruning were used to derive optimal subsets of descriptors for quantitative structure-pharmacokinetic relationship models. Models were trained on one set of compounds and validated with another. Absolute predicted ability was evaluated using a further independent test set of compounds. Correlations for test compounds ranged from 0.855 to 0.992. Predicted values agreed closely with experimental values for total clearance, renal clearance, and volume of distribution, while predictions for protein binding were encouraging. The combination of descriptor generation, ANNs, and the speed and success of this technique compared with conventional methods shows strong potential for use in pharmaceutical product development. [Copyright &y& Elsevier]

Purpose. Radial basis function artificial neural networks and theoretical descriptors were used to develop a quantitative structure pharmacokinetic relationship for structurally diverse drug compounds. Methods. Human bioavailability values were taken from the literature and descriptors were generated from the drug structures. All models were trained with 137 compounds and tested with a further 15, after which they were evaluated for predictive ability with an additional 15 compounds. Results. The final model possessed a 10-31-1 topology and training and testing correlation coefficients were 0.736 and 0.897, respectively. Predictions for independent compounds agreed well with experimental literature values, especially for compounds that were well absorbed and/or had high observed bioavailability. Important theoretical descriptors included solubility parameters, electronic descriptors, and topological indices. Conclusions. Useful information regarding drug bioavailability was gained from drug structure alone, reducing the need for experimental methods in drug development. [ABSTRACT FROM AUTHOR]

Biodistribution coefficients (BC) allow estimation of the tissue concentrations of proteins based on the plasma pharmacokinetics. We have previously established the BC values for monoclonal antibodies. Here, this concept is extended by development of a relationship between protein size and BC values. The relationship was built by deriving the BC values for various antibody fragments of known molecular weight from published biodistribution studies. We found that there exists a simple exponential relationship between molecular weight and BC values that allows the prediction of tissue distribution of proteins based on molecular weight alone. The relationship was validated by a priori predicting BC values of 4 antibody fragments that were not used in building the relationship. The relationship was also used to derive BC50 values for all the tissues, which is the molecular weight increase that would result in 50% reduction in tissue uptake of a protein. The BC50 values for most tissues were found to be ~35 kDa. An ability to estimate tissue distribution of antibody fragments based on the BC vs. molecular size relationship established here may allow better understanding of the biologics concentrations in tissues responsible for efficacy or toxicity. This relationship can also be applied for rational development of new biotherapeutic modalities with optimal biodistribution properties to target (or avoid) specific tissues.