Prediction of Human Pharmacokinetics from Animal Data and Molecular Structural Parameters using Multivariate Regression Analysis: Oral Clearance

Wajima, Toshihiro; Fukumura, Kazuya; Yano, Yoshitaka; Oguma, Takayoshi

doi:10.1002/jps.10510

Cited by 29 publications

(25 citation statements)

References 102 publications

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“…Over the years, many different approaches have been suggested to improve the prediction of clearance in humans. [4][5][6][7][8][9][10] It is now well established that simple allometry (SA) is not adequate for the prediction of clearance for drugs and based on the exponents of the SA, one may require correction factors (CF) 4,11 to improve the prediction of human drug clearance. In order to improve the prediction of human drug clearance from animal data, Mahmood and Balian, 4 developed the ''rule of exponents.''…”

Section: Introductionmentioning

confidence: 99%

Prediction of human drug clearance from animal data: Application of the rule of exponents and ‘fu corrected intercept method’ (FCIM)

Mahmood

2006

Journal of Pharmaceutical Sciences

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Section: Introductionmentioning

confidence: 99%

Prediction of human drug clearance from animal data: Application of the rule of exponents and ‘fu corrected intercept method’ (FCIM)

Mahmood

2006

Journal of Pharmaceutical Sciences

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“…Furthermore, in cases where molecular features indicate that the observed rat CL for a compound is likely to accurately extrapolate human CL, the need for additional animal studies would not be required to project human CL, resulting in a net reduction in animals and animal studies utilized in the drug discovery process. Additionally, the approach of combining in vivo and in silico methodologies is practically unprecedented; although previous studies have explored combining some aspects of in vivo and in silico data (Wajima et al, 2003), none have used the data in as direct a manner as exemplified here. This combination approach clearly adds substantial context and value to predictions made from preclinical data, and merits further consideration.…”

Section: Discussionmentioning

confidence: 99%

Extrapolation of Preclinical Pharmacokinetics and Molecular Feature Analysis of “Discovery-Like” Molecules to Predict Human Pharmacokinetics

Evans¹,

Jolivette²,

Nagilla³

et al. 2006

Drug Metab Dispos

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ABSTRACT:The prediction of human pharmacokinetics from preclinical species is an integral component of drug discovery. Recent studies with a 103-compound dataset suggested that scaling from monkey pharmacokinetic data tended to be the most accurate method for predicting human clearance. Additionally, interrogation of the twodimensional molecular properties of these molecules produced a set of associations which predict the likely extrapolative outcome (success or failure) of preclinical data to project human pharmacokinetics. However, a limitation of the previous analyses was the relative paucity of data for typical "discovery-like" molecules (molecular weight >300 and/or clogP >3). The objective of this investigation was to generate preclinical data required for extension of this dataset for additional discovery-like molecules and determine whether the aforementioned findings continue to apply for these molecules. In vivo nonrodent intravenous pharmacokinetic data were generated for 13 molecules, and data for 8 additional molecules were obtained from the literature. Additionally, the various scaling methodologies and molecular features analysis were applied to this new dataset to predict human pharmacokinetics. Whereas the predictive accuracies demonstrated across all of the various methodologies were lower for this higher clearance compound dataset, scaling from monkey liver blood flow continued to be an accurate methodology, and human volume of distribution was similarly well predicted regardless of scaling methodology. Lastly, application of the molecular feature associations, particularly data-dependent associations, afforded an improved predictivity compared with the liver blood flow scaling approaches, and provides insight into the extrapolation of high clearance compounds in the preclinical species to human.The use of in vivo preclinical pharmacokinetic data generated during lead optimization to predict human pharmacokinetic properties is commonplace in the pharmaceutical industry, with a variety of methodologies available for data predictivity (Ings, 1990;Pogessi, 2004). Recent investigations using a diverse set of 103 nonpeptide xenobiotics with rat, dog, monkey, and human intravenous pharmacokinetic data have yielded a number of interesting and useful observations, including the value of monkey pharmacokinetic data in successful prediction of human pharmacokinetics, and the relative lack of added value provided by dog pharmacokinetic data (Ward and Smith, 2004a,b). Additionally, when several quantitative methodologies, including liver blood flow-based scaling and body weight-based allometry, were applied to this dataset for predicting human clearance (CL), the greatest accuracy was achieved using the monkey liver blood flow approach, rather than allometry, even with the inclusion of mechanistic or empirical allometric correction factors (Nagilla and Ward, 2004). Finally, the availability of this comprehensive dataset has allowed the identification of a set of associations between preclinical pharmaco...

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“…7 parameters of interest for a variety of different drugs, and this method is considered to be more adequate. 12) In this study, we assumed that the off-hemodialysis clearance approximated the non-renal clearance, while the on-hemodialysis clearance was considered to be the sum of the offhemodialysis clearance and the hemodialytic clearance. The prediction of the pharmacokinetics in ESRD patients was evaluated based on allometry using 17 antibiotics.…”

Section: Prediction Of Pharmacokinetics In Esrd Patientsmentioning

confidence: 99%

“…6) For example, apparent oral clearances in humans could be adequately extrapolated from animal data, while the structural parameters could be evaluated by a multivariate analysis of various drugs selected in view of their structure, molecular weight, partition coefficient, number of hydrogen bond acceptors, pharmacological activities, and pharmacokinetic characteristics. 12) Antibiotics are suitable for this approach because they are diverse enough to evaluate the method in terms of various physicochemical and pharmacokinetic properties. 13) The purpose of this study was to provide a new method for the prediction of pharmacokinetics in ESRD patients and to confirm its validity and applicability using an experimental renal failure model.…”

mentioning

confidence: 99%

Prediction of Pharmacokinetics of Antibiotics in Patients with End-Stage Renal Disease

Tajima

Nagashima²,

Torii³

et al. 2006

Biological & Pharmaceutical Bulletin

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The pharmacokinetics in patients with end-stage renal disease (ESRD) is quite different from that in patients with normal renal function. Numerous approaches are undertaken to appropriately adjust the dosage regimens of renal excretory drugs in ESRD patients because the systemic exposure in this population unexpectedly increases and frequently causes adverse events. In contrast, hemodialysis sometimes results in an inadequate decrease of the plasma drug level from the therapeutic range.1-4) Consequently, pharmacokinetic information for ESRD patients is requisite in the process of new drug development in order to ascertain the effect of renal failure on tolerability and exposure and to determine the dosage regimen. 5)However, the clinical study of ESRD patients has not often been performed due to difficulties with patient enrollment. In such cases, prediction of the pharmacokinetics might be an alternative to clinical studies and provide useful information for optimizing the dosage regimen.Modeling and simulation have been explored by many scientists, 6,7) and recently virtual clinical trials based on pharmacokinetics and pharmacodynamics (PK/PD) modeling and subsequent simulation have been employed for decisionmaking in drug development prior to cost consuming phase III/IV trials. 8) In particular, this kind of methodology is informative for the development of antibiotic agents, since there is clear evidence that the pharmacological effect is well correlated to some PK/PD parameters. 9,10) Hemodialysis is a physical process that can be described by a mathematical model. 3,11) There are many factors affecting drug removal during hemodialysis, including molecular weight, lipid and water solubility, surface area, porosity of the dialysis membrane, blood and dialysate flow rates, protein binding and red blood cell partitioning. Even though some methods were previously developed considering these factors, they are complicated for clinical use. Thus, we are trying to develop a simple method based on several assumptions. Utilization of the chemical structure and pharmacokinetic relationship can be a useful approach for the prediction of pharmacokinetics.6) For example, apparent oral clearances in humans could be adequately extrapolated from animal data, while the structural parameters could be evaluated by a multivariate analysis of various drugs selected in view of their structure, molecular weight, partition coefficient, number of hydrogen bond acceptors, pharmacological activities, and pharmacokinetic characteristics.12) Antibiotics are suitable for this approach because they are diverse enough to evaluate the method in terms of various physicochemical and pharmacokinetic properties.13)The purpose of this study was to provide a new method for the prediction of pharmacokinetics in ESRD patients and to confirm its validity and applicability using an experimental renal failure model. A novel and convenient method to predict the pharmacokinetics of several kinds of antibiotic agents in patients with end-stage renal dise...

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Prediction of Human Pharmacokinetics from Animal Data and Molecular Structural Parameters using Multivariate Regression Analysis: Oral Clearance

Cited by 29 publications

References 102 publications

Prediction of human drug clearance from animal data: Application of the rule of exponents and ‘fu corrected intercept method’ (FCIM)

Prediction of human drug clearance from animal data: Application of the rule of exponents and ‘fu corrected intercept method’ (FCIM)

Extrapolation of Preclinical Pharmacokinetics and Molecular Feature Analysis of “Discovery-Like” Molecules to Predict Human Pharmacokinetics

Prediction of Pharmacokinetics of Antibiotics in Patients with End-Stage Renal Disease

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