James H. Wikel

Progress in predicting human ADME parameters in silico.

Understanding the development of a scientific approach is a valuable exercise in gauging the potential directions the process could take in the future. The relatively short history of applying computational methods to absorption, distribution, metabolism and excretion (ADME) can be split into defined periods. The first began in the 1960s and continued through the 1970s with the work of Corwin Hansch et al. Their models utilized small sets of in vivo ADME data. The second era from the 1980s through 1990s witnessed the widespread incorporation of in vitro approaches as surrogates of in vivo ADME studies. These approaches fostered the initiation and increase in interpretable computational ADME models available in the literature. The third era is the present were there are many literature data sets derived from in vitro data for absorption, drug-drug interactions (DDI), drug transporters and efflux pumps [P-glycoprotein (P-gp), MRP], intrinsic clearance and brain penetration, which can theoretically be used to predict the situation in vivo in humans. Combinatorial synthesis, high throughput screening and computational approaches have emerged as a result of continual pressure on pharmaceutical companies to accelerate drug discovery while decreasing drug development costs. The goal has become to reduce the drop-out rate of drug candidates in the latter, most expensive stages of drug development. This is accomplished by increasing the failure rate of candidate compounds in the preclinical stages and increasing the speed of nomination of likely clinical candidates. The industry now understands the reasons for clinical failure other than efficacy are mainly related to pharmacokinetics and toxicity. The late 1990s saw significant company investment in ADME and drug safety departments to assess properties such as metabolic stability, cytochrome P-450 inhibition, absorption and genotoxicity earlier in the drug discovery paradigm. The next logical step in this process is the evaluation of higher throughput data to determine if computational (in silico) models can be constructed and validated from it. Such models would allow an exponential increase in the number of compounds screened virtually for ADME parameters. A number of researchers have started to utilize in silico, in vitro and in vivo approaches in parallel to address intestinal permeability and cytochrome P-450-mediated DDI. This review will assess how computational approaches for ADME parameters have evolved and how they are likely to progress.

The use of neural networks for variable selection in QSAR

Abstract The application of a back-propagation neural network has been found to be an efficient and effective tool to identify pertinent variables for QSAR studies.

Experimental Designs for Selecting Molecules from Large Chemical Databases

Recent developments in high-throughput screening and combinatorial chemistry have generated interest in experimental design methods to select subsets of molecules from large chemical databases. In this manuscript three methods for selecting molecules from large databases are described:  edge designs, spread designs, and coverage designs. Two algorithms with linear time complexity that approximate spread and coverage designs are described. These algorithms can be threaded for multiprocessor systems, are compatible with any definition of molecular distance, and may be applied to very large chemical databases. For example, ten thousand molecules were selected using the maximum dissimilarity approximation to a spread design from a sixty-dimensional simulated molecular database of one million molecules in approximately 6 h on a UNIX workstation.

Depletion of cardiac norepinephrine in rats and mice by 1-methyl-4-phenyl-1,2,3,6-tetrahydropyridine (MPTP)

1-Methyl-4-phenyl-1,2,3,6-tetrahydropyridine (MPTP) is a commercially available chemical reagent. Although little has been known about its biological effects, recently MPTP has been reported to cause irreversible Parkinsons disease-like symptoms in humans and in monkeys. We describe here another pharmacologic effect of MPTP, the ability to deplete cardiac norepinephrine in rats and mice. In mice, cardiac norepinephrine concentration decreased within 1 hr, was maximally depleted at 24 hr, and recovered by 4-7 days after i.p. injection of a 32 mg/kg dose of MPTP. The depletion was antagonized by desipramine pretreatment, as was norepinephrine depletion by tyramine. In rats, cardiac norepinephrine depletion by 10-30 mg/kg, i.p., doses of MPTP was accompanied by depletion of cardiac dopamine and of norepinephrine in the mesenteric artery. In rats and in mice, norepinephrine in brain was affected to a smaller degree than was norepinephrine in heart, and dopamine in brain was depleted very little if at all. In spontaneously hypertensive rats, the depletion of cardiac norepinephrine was associated with a marked antihypertensive effect. The p-hydroxy analog of MPTP did not deplete cardiac norepinephrine in rats, indicating that its possible formation as a metabolite of MPTP was not involved in the depletion of cardiac norepinephrine. These findings extend the spectrum of known pharmacologic effects of MPTP.

Three-dimensional quantitative structure-permeability relationship analysis for a series of inhibitors of rhinovirus replication.

Multiple three-dimensional quantitative structure-activity relationship (3D-QSAR) approaches were applied to predicting passive Caco-2 permeability for a series of 28 inhibitors of rhinovirus replication. Catalyst, genetic function approximation (GFA) with MS-WHIM descriptors, CoMFA, and VolSurf were all used for generating 3D-quantitative structure permeability relationships utilizing a training set of 19 molecules. Each of these approaches was then compared using a test set of nine molecules not present in the training set. Statistical parameters for the test set predictions (r(2) and leave-one-out q(2)) were used to compare the models. It was found that the Catalyst pharmacophore model was the most predictive (test set of predicted versus observed permeability, r(2) = 0.94). This model consisted of a hydrogen bond acceptor, hydrogen bond donor, and ring aromatic feature with a training set correlation of r(2) = 0.83. The CoMFA model consisted of three components with an r(2) value of 0.96 and produced good predictions for the test set (r(2) = 0.84). VolSurf resulted in an r(2) value of 0.76 and good predictions for the test set (r(2) = 0.83). Test set predictions with GFA/WHIM descriptors (r(2) = 0.46) were inferior when compared with the Catalyst, CoMFA, and VolSurf model predictions in this evaluation. In summary it would appear that the 3D techniques have considerable value in predicting passive permeability for a congeneric series of molecules, representing a valuable asset for drug discovery.

Application of MS-WHIM Descriptors: 3. Prediction of Molecular Properties

MS-WHIM descriptors were developed in attempt to capture global 3D chemical information at molecular surface level. Initially, they contained information about size, shape and electrostatic distribution of a molecule. More recently they were enriched introducing new molecular surface properties related to hydrogen bonding capacity and hydrophobicity. This paper reports the application of expanded MS-WHIM descriptors to model: i) logP of 268 small organic molecules, ii) Caco-2 cell permeability of 17 heterogeneous compounds, and iii) pKa values of 15 substituted imidazoles. PLS regressions were derived and validated through cross-validation, repeated scrambling of the response variables, and test set predictions. The analysis of PLS models showed that MS-WHIM provided meaningful structure-property correlations: i) q2=0.709, ii) q2=0.797, and iii) q2=0.728. Hydrogen bonding capacity and hydrophobicity played a significant role and considerably improved the results. MS-WHIM descriptors, due to their holistic character, appear to be usefully applicable to a wide variety of chemical and biological problems.

Identification and Characterization of New Chemical Entities Targeting Apurinic/Apyrimidinic Endonuclease 1 for the Prevention of Chemotherapy-Induced Peripheral Neuropathy.

Chemotherapy-induced peripheral neuropathy (CIPN) is a potentially debilitating side effect of a number of chemotherapeutic agents. There are currently no U.S. Food and Drug Administration–approved interventions or prevention strategies for CIPN. Although the cellular mechanisms mediating CIPN remain to be determined, several lines of evidence support the notion that DNA damage caused by anticancer therapies could contribute to the neuropathy. DNA damage in sensory neurons after chemotherapy correlates with symptoms of CIPN. Augmenting apurinic/apyrimidinic endonuclease (APE)-1 function in the base excision repair pathway reverses this damage and the neurotoxicity caused by anticancer therapies. This neuronal protection is accomplished by either overexpressing APE1 or by using a first-generation targeted APE1 small molecule, E3330 [(2E)-2-[(4,5-dimethoxy-2-methyl-3,6-dioxo-1,4-cyclohexadien-1-yl)methylene]-undecanoic acid; also called APX3330]. Although E3330 has been approved for phase 1 clinical trials (Investigational New Drug application number IND125360), we synthesized novel, second-generation APE1-targeted molecules and determined whether they would be protective against neurotoxicity induced by cisplatin or oxaliplatin while not diminishing the platins’ antitumor effect. We measured various endpoints of neurotoxicity using our ex vivo model of sensory neurons in culture, and we determined that APX2009 [(2E)-2-[(3-methoxy-1,4-dioxo-1,4-dihydronaphthalen-2-yl)methylidene]-N,N-diethylpentanamide] is an effective small molecule that is neuroprotective against cisplatin and oxaliplatin-induced toxicity. APX2009 also demonstrated a strong tumor cell killing effect in tumor cells and the enhanced tumor cell killing was further substantiated in a more robust three-dimensional pancreatic tumor model. Together, these data suggest that the second-generation compound APX2009 is effective in preventing or reversing platinum-induced CIPN while not affecting the anticancer activity of platins.

LY249933: a cardioselective 1,4-dihydropyridine with positive inotropic activity.

Compound LY249933 and its component diastereomers, (RR) and (SR), were studied for their vascular and cardiac effects in vitro and in vivo. In guinea pig cardiac ventricular membranes, LY249933, (RR), and (SR) potently displaced bound [3H]nitrendipine (Kd values = 2–6 nM). In isolated guinea pig right ventricular strips, LY249933 produced a small but significant increase in contraction, whereas (RR) substantially increased (−log EC50 (M) = 4.6 ± 0.8) and (SR) decreased contraction (−log EC50 (M) = 4.1 ± 0.8). In isolated canine cephalic vein, contracted with 80 mM KC1, an increase in contraction was produced by (RR), whereas relaxation was produced by LY249933 (− log EC50 (M) = 5.9 ± 0.9) and (SR) (−log EC50 (M) = 6.0 ± 0.7). At 20 mM KC1, (RR) increased, (SR) decreased, but LY249933 did not alter contraction. In anesthetized dogs, LY249933 (200 μg/kg/min, i.v.) increased dP/dt60, decreased heart rate, but did not change vascular resistance or rate pressure product. At the same dose, (RR) and (SR) both tended to increase dP/dt60 nonsignificantly, whereas (RR) increased and (SR) decreased vascular resistance. Both (RR) and (SR) tended to decrease heart rate nonsignificantly, whereas (RR) did not change and (SR) decreased rate pressure product. Thus, LY249933 produced potentially beneficial cardiovascular changes resulting from the combined actions of its (RR) and (SR) diastereomers that are postulated to be calcium agonist and antagonist, respectively.

QSAR study of benzoquinolinones as inhibitors of human type 1 5-α-reductase.

Abstract QSAR models have been developed with regression analysis that related a specific lipophilic feature of the substituent of the molecule with biological activity. A second important feature of the compounds, the energy of the HOMO, was revealed using a simple visualization technique.

QSAR study of ortho-phenylphenol leukotriene B4 receptor antagonists

Abstract QSAR models for a series of o-phenylphenol LTB 4 receptor antagonist have been developed with regression analysis. These models related specific features, electronic, size and shape, of the substituent with biological activity. The models have predictive value and may be useful in assisting future synthetic direction within this series of compounds.