Purpose. To explore the quantitative structure pharmacokinetic relationships of the disposition parameters: clearance (CL), apparent volume of drug distribution (V-ap), fractal clearance (CLf), and fractal volume (v(f)) for 272 structurally unrelated drugs used in therapeutics. Methods. Literature data were used for CL and V-ap whereas CLf and v(f) were estimated as described previously (Pharm. Res. 18, 1056, 2001 and 19, 697, 2002). A variety of molecular descriptors expressing lipophilicity, ionization, molecular size and hydrogen bonding capacity were estimated using computer packages. The data were analyzed using multivariate statistics. For each disposition parameter (CL, V-ap, CLf, v(f)) PCA (principal component analysis) and PLS (projection to latent structures) were applied to the total set of data as well as to subsets of data. Results. Drugs were divided into two classes (I and II) according to their v(f) /V-ap ratio. Class I comprises 131 drugs with v(f) /V-ap >1, whereas class II 141 drugs with v(f) /V-ap < 1. After initial PLS analysis, class I was subdivided in subclusters I-a (30 drugs) and I-b (101 drugs). It was found that Ia included mostly acidic drugs with high protein binding, whereas class II comprises mainly basic, lipophilic compounds. No correlation was found between CL, V-ap, CLf and the used descriptors. Adequate PLS models were derived for v(f) considering subclusters I-a, I-b and class II separately. The low v(f) values of class I-a drugs were affected negatively from molecular size descriptors and non- polar surface area. For class I-b drugs with intermediate v(f) values, apparent lipophilicity contributed positively, although molecular size descriptors and polarity were inhibitory factors. The high v(f) values of class II drugs were positively dependent on intrinsic lipophilicity and increased basicity, whereas polarity entered with negative contribution. Conclusions. The parameters V-ap, CL, and CLf fail to reflect the physicochemical properties of drugs. The transformation of V-ap values to v(f) is the underlying cause for the valid models for v(f). These models allow a global consideration of molecular properties (lipophilicity, ionization, molecular size, polar surface area) which govern the distribution of drugs in the human body. The present study provides additional evidence for the physiologically sound concept of v(f)

Purpose. (i) Evaluate the predictive performance of the fractal volume of drug distribution, v(f), (Pharm. Res.18, 1056, 2001), (ii) develop the concept of the fractal clearance, CLf, which is the clearance analogue of v(f), (iii) examine the utility of CLf in allometric studies, (iv) develop allometric relationships for the elimination half-life, t(1/2), and (v) evaluate the use of v(f) and CLf in predicting the volume of drug distribution, V-ap, clearance, CL, and elimination half-life, t(1/2). Methods. Estimates for v(f) of various drugs were obtained and correlated with body mass using data only from animal species. A comparison was made between the predicted and actual v(f) values for humans. For a variety of animal species CLf values were estimated from the equation: CLf = v(f)/V-ap CL The allometric equations developed using CLf were compared with other allometric approaches. Allometric equations were also developed for t(1)/2 utilizing the allometric relationships of v(f) and CLf. Results. The predicted estimates of v(f) were very close to the actual values and the correlation exhibited favorable statistical properties. The values of the allometric exponents for CLf were found to be close to 0.75. The predictive performance for CL using the allometric equations for CLf in conjuction with the rule of exponents was found to be better than the currently considered most accurate allometric approaches. The values of the allometric exponents for t(1)/2 were found to be close to 0.25. Conclusion. The predictive ability of v(f) is high; predictions for V(a)p based on v(f) values are better than the current approaches. CLf expressed a good behavior both in prospective and retrospective analysis. The allometric exponents, 0.75, 0.25 for CLf and t(1/2), respectively, agree with the theoretical expected values.

The computational approach is one of the newest and fastest developing techniques in pharmacokinetics, ADME (absorption, distribution, metabolism, excretion) evaluation, drug discovery and toxicity. However, to date, the software packages devoted to ADME prediction, especially of metabolism, have not yet been adequately validated and still require improvements to be effective. Most are `open' systems, under constant evolution and able to incorporate rapidly, and often easily, new information from user or developer databases. Quantitative in silico predictions are now possible for several pharmacokinetic (PK) parameters, particularly absorption and distribution. The emerging consensus is that the predictions are no worse than those made using in vitro tests, with the decisive advantage that much less investment in technology, resources and time is needed. In addition, and of critical importance, it is possible to screen virtual compounds. Some packages are able to handle thousands of molecules in a few hours. However, common experience shows that, in part at least for essentially irrational reasons, there is currently a lack of confidence in these approaches. An effort should be made by the software producers towards more transparency, in order to improve the confidence of their consumers. It seems highly probable that in silico approaches will evolve rapidly, as did in vitro methods during the last decade. Past experience with the latter should be helpful in avoiding repetition of similar errors and in taking the necessary steps to ensure effective implementation. A general concern is the lack of access to the large amounts of data on compounds no longer in development, but still kept secret by the pharmaceutical industry. Controlled access to these data could be particularly helpful in validating new in silico approaches. (C) 2002 Elsevier Science B.V. All rights reserved.