[Evaluation of Prognostic Factors in Patients with Relapse and Unresectable Biliary Tract Cancer, Treated with S-1 after Failure of Gemcitabine].

Prognostic factors for patients with relapse and unresectable biliary tract cancer, treated with Tegafur Gimeracil Oteracil Potassium (S-1) after failure of gemcitabine (GEM), are unclear. We searched for prognostic factors in patients with relapse and unresectable biliary tract cancer treated with S-1 after failure of GEM, and investigated the relationship between prognostic factors, and the therapeutic effect of S-1. We retrospectively analyzed data of 33 patients with relapse and unresectable biliary tract cancer treated with S-1 after failure of GEM treatment. Statistically significant prognostic factors were extracted using Cox's proportional hazard model. Data was also collected on prognostic factors prior to the first dose of S-1, final prescription of S-1, and end of treatment. Changes in prognostic factors before the first dose of S-1, at final prescription of S-1, and at the end of treatment were evaluated using the Friedman test. Multivariate analysis identified neutrophil-lymphocyte ratio (NLR) [hazard ratio (HR)=4.599, p=0.004] and prognostic nutritional index (PNI) (HR=4.985, p=0.004) as independent poor prognostic factors for overall survival. Regarding the relationship between the therapeutic effect and prognostic factors, a significant change was observed in the change in PNI value from first administration of S-1 to the end of treatment (p=0.002). NLR and PNI are suggested to be prognostic factors in patients with relapse and unresectable biliary tract cancer, treated with S-1 after failure of GEM. Changes in PNI from the start of administration of S-1 may be related to therapeutic efficacy.

The feasibility of an efficient drug design method with high-performance computers.

In this study, we propose a supercomputer-assisted drug design approach involving all-atom molecular dynamics (MD)-based binding free energy prediction after the traditional design/selection step. Because this prediction is more accurate than the empirical binding affinity scoring of the traditional approach, the compounds selected by the MD-based prediction should be better drug candidates. In this study, we discuss the applicability of the new approach using two examples. Although the MD-based binding free energy prediction has a huge computational cost, it is feasible with the latest 10 petaflop-scale computer. The supercomputer-assisted drug design approach also involves two important feedback procedures: The first feedback is generated from the MD-based binding free energy prediction step to the drug design step. While the experimental feedback usually provides binding affinities of tens of compounds at one time, the supercomputer allows us to simultaneously obtain the binding free energies of hundreds of compounds. Because the number of calculated binding free energies is sufficiently large, the compounds can be classified into different categories whose properties will aid in the design of the next generation of drug candidates. The second feedback, which occurs from the experiments to the MD simulations, is important to validate the simulation parameters. To demonstrate this, we compare the binding free energies calculated with various force fields to the experimental ones. The results indicate that the prediction will not be very successful, if we use an inaccurate force field. By improving/validating such simulation parameters, the next prediction can be made more accurate.

Molecular dynamics simulation-based evaluation of the binding free energies of computationally designed drug candidates: importance of the dynamical effects.

The computational structure-based drug design (SBDD) mainly aims at generating or discovering new chemical compounds with sufficiently large binding free energy. In any de novo drug design methods and virtual screening methods, drug candidates are selected by approximately evaluating the binding free energy (or the binding affinity). This approximate binding free energy, usually called "empirical score," is critical to the success of the SBDD. The purpose of this work is to yield physical insight into the approximate evaluation method in comparison with an exact molecular dynamics (MD) simulation-based method (named MP-CAFEE), which can predict binding free energies accurately. We calculate the binding free energies for 58 selected drug candidates with MP-CAFEE. Here, the compounds are generated by OPMF, a novel fragment-based de novo drug design method, and the ligand-protein interaction energy is used as an empirical score. The results show that the correlation between the binding free energy and the interaction energy is not strong enough to clearly distinguish compounds with nM-affinity from those with µM-affinity. This implies that it is necessary to take into account the natural protein motion with explicitly surrounded by water molecules to improve the efficiency of the drug candidate selection procedure.

[Relationship between the effectiveness and dose of micafungin in the treatment of candidemia].

The Guidelines for Management of Deep-seated Mycoses 2007 recommend the use of micafungin as a first-line agent for the treatment of candidemia. On the package insert, the recommended dose of micafungin is 50 mg/d. However, the Guidelines recommend a micafungin dose of 100-150 mg/d. In the present study, we evaluated the relationship between the effectiveness and dose of micafungin in 42 patients with candidemia who underwent treatment with micafungin. We found that the efficacy rate of micafungin at a dose of 50 mg/d was 40%, whereas that at ≥100 mg/d was 87.5%. Moreover, the treatment was more effective in patients who received ≥100 mg/d of micafungin as compared to those who received 50 mg/d of micafungin. Furthermore, we assessed the efficacy of micafungin according to the Candida species. Among all patients, the efficacy rate of micafungin was found to be lower in patients infected by Candida parapsilosis as compared to those infected by other Candida species. However, among the patients who received ≥100 mg/d of micafungin, the efficacy rate in patients infected by Candida parapsilosis was equivalent to that of patients who were infected by other Candida species. Thus, based on the results of the present study, the optimal micafungin dose for the treatment of candidemia appears to range from 100 to 150 mg/d, as recommended by the Guidelines.

[Adverse events during pemetrexed administration caused by concomitant nonsteroid anti-inflammatory therapy].

Pemetrexed, a folate metabolic antagonist, is considered to be effective against plural mesotheliomas, non-small cell lung cancer, and especially for non-squamous cell cancer. However, it has been reported to have adverse interactions with nonsteroid anti-inflammatory drugs(NSAIDs). In the present study, we compared the incidence of adverse events between patients receiving pemetrexed therapy with and without concomitant NSAID administration. No significant difference in the incidence of hematotoxic events of Grade 3 or worse was observed. As for the incidence of non-hematotoxic events, the increase in the amount of creatinine, namely a severe adverse effect of Grade 2 or more, was significantly higher in the combined therapy group (p=0.018). No other significant differences were noted for other adverse events. A creatinine increase to Grade 2 or greater developed significantly earlier in the combined group(median value, 12.7 courses; p=0.0063). Our results suggest that renal dysfunction may easily develop as a result of continued pemetrexed administration combined with NSAID therapy. Therefore, it is necessary to take precautions against adverse side effects such as renal dysfunction when combining pemetrexed with NSAID therapy, by conducting periodic examinations.

Large-scale integrated super-computing platform for next generation virtual drug discovery.

Traditional drug discovery starts by experimentally screening chemical libraries to find hit compounds that bind to protein targets, modulating their activity. Subsequent rounds of iterative chemical derivitization and rescreening are conducted to enhance the potency, selectivity, and pharmacological properties of hit compounds. Although computational docking of ligands to targets has been used to augment the empirical discovery process, its historical effectiveness has been limited because of the poor correlation of ligand dock scores and experimentally determined binding constants. Recent progress in super-computing, coupled to theoretical insights, allows the calculation of the Gibbs free energy, and therefore accurate binding constants, for usually large ligand-receptor systems. This advance extends the potential of virtual drug discovery. A specific embodiment of the technology, integrating de novo, abstract fragment based drug design, sophisticated molecular simulation, and the ability to calculate thermodynamic binding constants with unprecedented accuracy, are discussed.

HLA class I distribution in Japanese patients with schizophrenia.

Several Caucasian studies and one Japanese study have observed associations between human leukocyte antigen (HLA) class I specificities, including A24 (9) and A26 (10) and schizophrenia. Most of those studies were conducted in 1970s and early 1980s, when the typing technique of HLA was not adequately reliable. Also, an operational diagnostic system was not employed in many of the studies. The present study investigated frequencies of HLA-A specificities in schizophrenia patients (ICD-10 and DSM-III-R, n=98) and sex-matched healthy controls (n=392) from population in the southwestern part of Japan. HLA-B and -C specificities were studied in addition. Frequencies of subjects possessing A24 and A26 were not different between the patients and controls (54% and 24% in the patients and 62% and 24% in the controls, respectively). No significant difference was found in frequencies of other class I (A, B, and C) specificities between the patients and the controls. Thus, the present study provided no evidence for an association between the HLA class I specificities, including A24, A26, and others, and schizophrenia in the Japanese population.