Dietary Intake during 56 Weeks of a Low-Fat Diet for Lomitapide Treatment in Japanese Patients with Homozygous Familial Hypercholesterolemia.

AIM: Lomitapide is an oral inhibitor of the microsomal triglyceride transfer protein used to treat homozygous familial hypercholesterolemia (HoFH); patients require a low-fat diet to minimize gastrointestinal adverse effects and dietary supplements to prevent nutrient deficiencies. We investigated the diet and nutritional status during lomitapide treatment. METHODS: Japanese patients with HoFH, who were in a phase 3 trial of lomitapide, were instructed to start low-fat diets with supplements of vitamin E and essential fatty acids 6 weeks before starting lomitapide treatment. Dietary education was conducted by registered dietitians 16 times during the study period, which included a pre-treatment run-in phase (Weeks －6-0), a lomitapide treatment efficacy phase (Weeks 0-26) and a safety phase (Weeks 26-56). Two-day dietary records were collected at each dietary counseling session. Anthropometric and biochemical parameters were measured at Weeks 0, 26 and 56. RESULTS: Eight patients completed the 56 weeks of lomitapide treatment. Their median energy intakes derived from lipids were 19.2% and 17.9% during the efficacy and safety phases, respectively. "Fats and oils" intakes, and "Fatty meat and poultry" intakes in two patients, were successfully reduced to achieve low-fat diets. Although intakes of energy, fatty acids and fat-soluble vitamins did not differ significantly among phases, body weight, serum fatty acid levels and vitamin E concentrations were decreased at Week 26 as compared with Week 0. CONCLUSION: HoFH patients can adhere to low-fat diets with ongoing dietary counseling. Instructions about intakes of energy, fatty acids and fat-soluble vitamins, as well as periodic evaluations of nutritional status, are necessary.

Improvement of quantitative structure-activity relationship (QSAR) tools for predicting Ames mutagenicity: outcomes of the Ames/QSAR International Challenge Project.

The International Conference on Harmonization (ICH) M7 guideline allows the use of in silico approaches for predicting Ames mutagenicity for the initial assessment of impurities in pharmaceuticals. This is the first international guideline that addresses the use of quantitative structure-activity relationship (QSAR) models in lieu of actual toxicological studies for human health assessment. Therefore, QSAR models for Ames mutagenicity now require higher predictive power for identifying mutagenic chemicals. To increase the predictive power of QSAR models, larger experimental datasets from reliable sources are required. The Division of Genetics and Mutagenesis, National Institute of Health Sciences (DGM/NIHS) of Japan recently established a unique proprietary Ames mutagenicity database containing 12140 new chemicals that have not been previously used for developing QSAR models. The DGM/NIHS provided this Ames database to QSAR vendors to validate and improve their QSAR tools. The Ames/QSAR International Challenge Project was initiated in 2014 with 12 QSAR vendors testing 17 QSAR tools against these compounds in three phases. We now present the final results. All tools were considerably improved by participation in this project. Most tools achieved >50% sensitivity (positive prediction among all Ames positives) and predictive power (accuracy) was as high as 80%, almost equivalent to the inter-laboratory reproducibility of Ames tests. To further increase the predictive power of QSAR tools, accumulation of additional Ames test data is required as well as re-evaluation of some previous Ames test results. Indeed, some Ames-positive or Ames-negative chemicals may have previously been incorrectly classified because of methodological weakness, resulting in false-positive or false-negative predictions by QSAR tools. These incorrect data hamper prediction and are a source of noise in the development of QSAR models. It is thus essential to establish a large benchmark database consisting only of well-validated Ames test results to build more accurate QSAR models.