No abstract

Predicting blood-brain barrier (BBB) permeation remains a challenge in drug design. Since it is impossible to determine experimentally the BBB partitioning of large numbers of preclinical candidates, alternative evaluation methods based on computerized models are desirable. The present study was conducted to demonstrate the value of descriptors derived from 3D molecular fields in estimating the BBB permeation of a large set of compounds and to produce a simple mathematical model suitable for external prediction. The method used (VolSurf) transforms 3D fields into descriptors and correlates them to the experimental permeation by a discriminant partial least squares procedure. The model obtained here correctly predicts more than 90% of the BBB permeation data. By quantifying the favorable and unfavorable contributions of physicochemical and structural properties, it also offers valuable insights for drug design, pharmacological profiling, and screening. The computational procedure is fully automated and quite fast. The method thus appears as a valuable new tool in virtual screening where selection or prioritization of candidates is required from large collections of compounds.

Imatinib is effective for the treatment of chronic myeloid leukemia (CML). However even undetectable BCR-ABL1 by Q-RT-PCR does not equate to eradication of the disease. Digital-PCR (dPCR), able to detect 1 BCR-ABL1 positive cell out of 10 7 , has been recently developed. The ISAV study is a multicentre trial aimed at validating dPCR to predict relapses after imatinib discontinuation in CML patients with undetectable Q-RT-PCR. CML patients under imatinib therapy since more than 2 years and with undetectable PCR for at least 18 months were eligible. Patients were monitored by standard Q-RT-PCR for 36 months. Patients losing molecular remission (two consecutive positive Q-RT-PCR with at least 1 BCR-ABL1/ABL1 value above 0.1%) resumed imatinib. The study enrolled 112 patients, with a median follow-up of 21.6 months. Fifty-two of the 108 evaluable patients (48.1%), relapsed; 73.1% relapsed in the first 9 months but 14 late relapses were observed between 10 and 22 months. Among the 56 not-relapsed patients, 40 (37.0% of total) regained Q-RT-PCR positivity but never lost MMR. dPCR results showed a significant negative predictive value ratio of 1.115 [95% CI: 1.013-1.227]. An inverse relationship between patients age and risk of relapse was evident: 95% of patients <45 years relapsed versus 42% in the class 45 to <65 years and 33% of patients 65 years [P(v 2 ) < 0.0001]. Relapse rates ranged between 100% (<45 years, dPCR1) and 36% (>45 years, dPCR-). Imatinib can be safely discontinued in the setting of continued PCR negativity; age and dPCR results can predict relapse.

The relationship of rotatable bond count (N(rot)) and polar surface area (PSA) with oral bioavailability in rats was examined for 434 Pharmacia compounds and compared with an earlier report from Veber et al. (J. Med. Chem. 2002, 45, 2615). N(rot) and PSA were calculated with QikProp or Cerius2. The resulting correlations depended on the calculation method and the therapeutic class within the data superset. These results underscore that such generalizations must be used with caution.

Multidrug resistance mediated by ATP binding cassette (ABC) transporters such as P-glycoprotein (P-gp) represents a serious problem for the development of effective anticancer drugs. In addition, P-gp has been shown to reduce oral absorption, modulate hepatic, renal, or intestinal elimination, and restrict blood-brain barrier penetration of several drugs. Consequently, there is a great interest in anticipating whether drug candidates are P-gp substrates or inhibitors. In this respect, two different computational models have been developed. A method for discriminating P-gp substrates and nonsubstrates has been set up based on calculated molecular descriptors and multivariate analysis using a training set of 53 diverse drugs. These compounds were previously classified as P-gp substrates or nonsubstrates on the basis of the efflux ratio from Caco-2 permeability measurements. The program Volsurf was used to compute the compounds' molecular descriptors. The descriptors were correlated to the experimental classes using partial least squares discriminant analysis (PLSD). The model was able to predict correctly the behavior of 72% of an external set of 272 proprietary compounds. Thirty of the 53 previously mentioned drugs were also evaluated for P-gp inhibition using a calcein-AM (CAM) assay. On the basis of these additional P-gp functional data, a PLSD analysis using GRIND-pharmacophore-based descriptors was performed to model P-gp substrates having poor or no inhibitory activity versus inhibitors having no evidence of significant transport. The model was able to discriminate between 69 substrates and 56 inhibitors taken from the literature with an average accuracy of 82%. The model allowed also the identification of some key molecular features that differentiate a substrate from an inhibitor, which should be taken into consideration in the design of new candidate drugs. These two models can be implemented in a virtual screening funnel.

In this review, we first examine the contextual background of structure-pharmacokinetic relationships. Some concepts in drug disposition are briefly recalled, and inherent difficulties in structure-pharmacokinetic relationships are outlined. Lipophilicity is then investigated in the light of the intermolecular and intramolecular interactions it encodes. In the main body of the review, a number of pharmacokinetic processes are examined for their relations with lipophilicity. These processes are taken in a logical sequence of permeation, absorption (intestine, skin, cornea, brain), plasma protein binding, tissue distribution, volume of distribution and renal clearance. Relations between metabolism and lipophilicity are more complex, since biotransformation involves both low-energy (enzyme binding) and high-energy (catalysis) processes. Only the former may be related to lipophilicity. The conclusion argues against faulty statistics and over-interpretation.

No abstract

Treatment of mCRC with TMZ driven by MGMT promoter hypermethylation in AT samples did not provide meaningful PFS rate at 12 weeks. This biomarker changed from AT to BT, indicating that testing BT biopsy or plasma is needed for refined target selection.