homoharringtonine and Chemical-and-Drug-Induced-Liver-Injury

To assess the efficacy and toxicity of HAA regimen (Homoharringtonine 4 mg/m(2)/day, days 1-3; cytarabine 150 mg/m(2)/day, days 1-7; aclarubicin 12 mg/m(2)/day, days 1-7) as a salvage therapy in the treatment of refractory and/or relapsed acute myeloid leukemia (AML), 46 patients with refractory and/or relapsed AML, median age 37 (16-65) years, participated in this clinical study. The median follow-up was 41 (10-86) months. Eighty percent of patients achieved complete remission (CR), and the first single course of re-induction HAA regimen resulted in CR rate of 76.1 %. The study protocol allowed two courses of induction. The CR rates of patients with favorable, intermediate and unfavorable cytogenetics were 90 %, 88.9 %, and 37.5 %, respectively. For all patients, the estimated 3-year overall survival (OS) rate was 42 %, and the estimated relapse free survival (RFS) at 3 years for the 36 CR cases was 49 %. The toxicities associated with HAA regimen were acceptable. HAA is a good choice in cases with refractory/relapsing AML for salvage chemotherapy, preferably with a high-efficacy and low-toxicity profile.

Topics: Aclarubicin; Adolescent; Adult; Aged; Antineoplastic Combined Chemotherapy Protocols; Chemical and Drug Induced Liver Injury; Combined Modality Therapy; Cytarabine; Disease-Free Survival; Drug Resistance, Neoplasm; Female; Follow-Up Studies; Harringtonines; Heart Diseases; Homoharringtonine; Humans; Kaplan-Meier Estimate; Kidney Diseases; Leukemia, Myeloid, Acute; Leukemia, Myelomonocytic, Acute; Male; Middle Aged; Recurrence; Remission Induction; Salvage Therapy; Stem Cell Transplantation; Young Adult

Drug-induced liver injury is one of the main causes of drug attrition. The ability to predict the liver effects of drug candidates from their chemical structures is critical to help guide experimental drug discovery projects toward safer medicines. In this study, we have compiled a data set of 951 compounds reported to produce a wide range of effects in the liver in different species, comprising humans, rodents, and nonrodents. The liver effects for this data set were obtained as assertional metadata, generated from MEDLINE abstracts using a unique combination of lexical and linguistic methods and ontological rules. We have analyzed this data set using conventional cheminformatics approaches and addressed several questions pertaining to cross-species concordance of liver effects, chemical determinants of liver effects in humans, and the prediction of whether a given compound is likely to cause a liver effect in humans. We found that the concordance of liver effects was relatively low (ca. 39-44%) between different species, raising the possibility that species specificity could depend on specific features of chemical structure. Compounds were clustered by their chemical similarity, and similar compounds were examined for the expected similarity of their species-dependent liver effect profiles. In most cases, similar profiles were observed for members of the same cluster, but some compounds appeared as outliers. The outliers were the subject of focused assertion regeneration from MEDLINE as well as other data sources. In some cases, additional biological assertions were identified, which were in line with expectations based on compounds' chemical similarities. The assertions were further converted to binary annotations of underlying chemicals (i.e., liver effect vs no liver effect), and binary quantitative structure-activity relationship (QSAR) models were generated to predict whether a compound would be expected to produce liver effects in humans. Despite the apparent heterogeneity of data, models have shown good predictive power assessed by external 5-fold cross-validation procedures. The external predictive power of binary QSAR models was further confirmed by their application to compounds that were retrieved or studied after the model was developed. To the best of our knowledge, this is the first study for chemical toxicity prediction that applied QSAR modeling and other cheminformatics techniques to observational data generated by the means of automate

Topics: Animals; Chemical and Drug Induced Liver Injury; Cluster Analysis; Databases, Factual; Humans; MEDLINE; Mice; Models, Chemical; Molecular Conformation; Quantitative Structure-Activity Relationship