Pharmacodynamic differences between species exemplified by the novel anticancer agent CHS 828

When a candidate drug enters clinical trials, decisions regarding dosing are mainly based on animal data. Occasionally, toxicity problems are faced in the clinic because of unexpected species differences in pharmacokinetics or pharmacodynamics between humans and preclinical species. Fludarabine and topotecan are examples of such drugs. In the first clinical trials of the new agent CHS 828, the maximum tolerated dose was reached earlier than expected from animal data. This paper discusses the issue of species differences in the development of anticancer drugs, and preclinical models for detection and quantification of such differences. Pharmacokinetic and hematological toxicity data of CHS 828 from studies in rats and humans are presented. In vitro sensitivity to CHS 828 and some established cytotoxic agents was measured in lymphocytes from humans and rats and in a panel of human and rodent cell‐lines. 10–100 times higher CHS 828 exposure was tolerated by rats than by patients. In both in vitro cell systems, CHS 828 showed higher potency in human cells compared to rodent cells. A species difference was evident also for fludarabine, but not for doxorubicin and cisplatin. CHS 828 pharmacokinetics were similar across species. In conclusion, the lower tolerance of CHS 828 in humans than in rats could be detected in vitro in cultures of peripheral lymphocytes. Preclinical studies of species differences could help the interpretation of in vivo effect studies as well as the choice of starting dose for clinical trials. We suggest peripheral lymphocytes from different species as a potential model system for such studies. Drug Dev. Res. 61:218–226, 2004. © 2004 Wiley‐Liss, Inc.     

Chlamydial antibodies and risk of prostate cancer.

OBJECTIVE: We assessed the risk of prostate cancer by exposure to Chlamydia trachomatis. METHOD: Seven hundred thirty eight cases of prostate cancer and 2,271 matched controls were identified from three serum sample banks in Finland, Norway, and Sweden by linkage to the population based cancer registries. RESULTS: A statistically significant inverse association (odds ratio, 0.69; 95% confidence interval, 0.51-0.94) was found. It was consistent by different serotypes and there was a consistent dose-response relationship. CONCLUSION: C. trachomatis infection is not likely to increase the risk of prostate cancer. Whether the inverse relationship is true or due to difficulties in measuring the true exposure in prostatic tissue by serology, confounders or other sources of error remain open.     

Antitumor efficacy and acute toxicity of the novel dipeptide melphalanyl-p-L-fluorophenylalanine ethyl ester (J1) in vivo

The novel alkylating dipeptide melphalanyl-p-L-fluorophenylalanine ethyl ester (J1) was evaluated for acute toxicity and antitumor activity in mice, with melphalan as a reference. To determine a safe and tolerable dose for efficacy studies the acute toxicity following intravenous injection in the tail vein was monitored using a 14-day schedule with up to four doses. The highest tested dose, 25 micromoles/kg, was considered close to this level, with minor effects on body weight gain but significant effects on hematological parameters. Melphalan and J1 appeared equitoxic with no statistically significant differences. Subsequently a mouse hollow fiber model was employed with subcutaneous implantation of fibers containing human tumor cells. Three different human tumor cell lines as well as two samples of primary human tumor cells (ovarian carcinoma and chronic lymphatic leukemia) were used as tumor models. At the dose level tested there was a marked and statistically significant decrease in both T-cell leukemia CCRF-CEM and small cell lung cancer NCI-H69 tumor cell growth and viability in response to J1 as compared with both placebo and melphalan treated groups. In primary ovarian carcinoma cells only J1 treatment resulted in significant tumor regression (net cell kill). In summary the results indicate that, despite an expected short half time in the blood circulation, the promising in vitro data from the previous studies of J1 seems translatable into the in vivo situation. At equal doses of alkylating units J1, compared to melphalan, was more active in the mouse hollow-fiber model, but showed similar general toxicity.     

Genetic and environmental control of host-gut microbiota interactions

Genetics provides a potentially powerful approach to dissect host-gut microbiota interactions. Toward this end, we profiled gut microbiota using 16s rRNA gene sequencing in a panel of 110 diverse inbred strains of mice. This panel has previously been studied for a wide range of metabolic traits and can be used for high-resolution association mapping. Using a SNP-based approach with a linear mixed model, we estimated the heritability of microbiota composition. We conclude that, in a controlled environment, the genetic background accounts for a substantial fraction of abundance of most common microbiota. The mice were previously studied for response to a high-fat, high-sucrose diet, and we hypothesized that the dietary response was determined in part by gut microbiota composition. We tested this using a cross-fostering strategy in which a strain showing a modest response, SWR, was seeded with microbiota from a strain showing a strong response, A×B19. Consistent with a role of microbiota in dietary response, the cross-fostered SWR pups exhibited a significantly increased response in weight gain. To examine specific microbiota contributing to the response, we identified various genera whose abundance correlated with dietary response. Among these, we chose Akkermansia muciniphila, a common anaerobe previously associated with metabolic effects. When administered to strain A×B19 by gavage, the dietary response was significantly blunted for obesity, plasma lipids, and insulin resistance. In an effort to further understand host-microbiota interactions, we mapped loci controlling microbiota composition and prioritized candidate genes. Our publicly available data provide a resource for future studies.Studies carried out over the last decade have revealed that gut microbiota contribute to a variety of common disorders, including obesity and diabetes (Musso et al. 2011), colitis (Devkota et al. 2012), atherosclerosis (Wang et al. 2011), rheumatoid arthritis (Vaahtovuo et al. 2008), and cancer (Yoshimoto et al. 2013). The evidence for metabolic interactions is particularly strong, as a large body of data now supports the conclusion that gut microbiota influence the energy harvest from dietary components, particularly complex carbohydrates, and that metabolites such as the short-chain fatty acids produced by gut bacteria can perturb metabolic traits, including adiposity and insulin resistance (Turnbaugh et al. 2006, 2009; Backhed et al. 2007; Wen et al. 2008; Ridaura et al. 2013). Gut microbiota communities are assembled each generation, influenced by maternal seeding, environmental factors, host genetics, and age, resulting in substantial variations in composition among individuals in human populations (Eckburg et al. 2005; Costello et al. 2009; Human Microbiome Project Consortium 2012; Goodrich et al. 2014). Most experimental studies of host-gut microbiota interactions have employed large perturbations, such as comparisons of germ-free versus conventional mice, and the significance of common variations in gut microbiota composition for disease susceptibility is still poorly understood. Furthermore, while studies with germ-free mice have clearly implicated microbiota in clinically relevant traits, it has proven difficult to identify the responsible taxa of bacteria.We now report a population-based analysis of host-gut microbiota interactions in the mouse. One of the issues we explore is the role of host genetics. Although some evidence is consistent with significant heritability of gut microbiota composition, the extent to which the host controls microbiota composition under controlled environmental conditions is unclear. We also examined the role of common variations in gut microbiota in metabolic traits such as obesity and insulin resistance and mapped loci contributing to the abundance of certain microbiota. We performed our study using a resource termed the Hybrid Mouse Diversity Panel (HMDP), consisting of about 100 inbred strains of mice that have been either sequenced or subjected to high-density genotyping (Bennett et al. 2010). The resource has several advantages for genetic analysis as compared to traditional genetic crosses. First, it allows high-resolution mapping by association rather than linkage analysis, and it has now been used for the identification of a number of novel genes underlying complex traits (Farber et al. 2011; Lavinsky et al. 2015; Parks et al. 2015; Rau et al. 2015). Second, since the strains are permanent, the data from separate studies can be integrated, allowing the development of large, publicly available databases of physiological and molecular traits relevant to a variety of clinical disorders (systems.genetics.ucla.edu and phenome.jax.org). Third, the panel is ideal for examining gene-by-environment interactions, since it is possible to examine individuals of a particular genotype under a variety of conditions (Orozco et al. 2012; Parks et al. 2013).     

Endogenous aggregates of amyloidogenic cystatin C variant are removed by THP-1 cells in vitro and induce differentiation and a proinflammatory response

A mutation in the human cystatin C gene leads to familial cerebral amyloid angiopathy. This disease is known as “hereditary cerebral hemorrhage with amyloidosis-Icelandic type” or “hereditary cystatin C amyloid angiopathy.” The mutant cystatin C protein forms aggregates and amyloid, within the central nervous system almost exclusively in connection with the vascular system. It was not known whether immune cells could remove mutant cystatin C protein aggregates. Ex vivo mutant cystatin C protein aggregates, both in solution and dried onto a glass surface, induced adhesion to the substrate, differentiated the THP-1 monocyte cell line and led to a proinflammatory response. Aggregates were also taken up by both THP-1 cells and THP-1 derived macrophages. These are the same responses induced by other amyloidogenic protein species, such as amyloid β protein and amylin, supporting the model of all amyloidogenic proteins being toxic due to common structural motifs. Proinflammatory response induced by the ex vivo mutant cystatin C protein aggregates suggests that vascular inflammation plays an important role in hereditary cerebral hemorrhage with amyloidosis-Icelandic type. Ex vivo protein aggregates of cystatin C might better model cellular behavior than in vitro-generated aggregates or supplement in vitro material.     

Granzyme D Is a Novel Murine Mast Cell Protease That Is Highly Induced by Multiple Pathways of Mast Cell Activation

Granzymes are serine proteases known mostly for their role in the induction of apoptosis. Granzymes A and B have been extensively studied, but relatively little is known about granzymes C to G and K to M. T cells, lymphohematopoietic stromal cells, and granulated metrial gland cells express granzyme D, but the function of granzyme D is unknown. Here we show that granzyme D is expressed by murine mast cells and that its level of expression correlates positively with the extent of mast cell maturation. Coculture of mast cells with live, Gram-positive bacteria caused a profound, Toll-like receptor 2 (TLR2)-dependent induction of granzyme D expression. Granzyme D expression was also induced by isolated bacterial cell wall components, including lipopolysaccharide (LPS) and peptidoglycan, and by stem cell factor, IgE receptor cross-linking, and calcium ionophore stimulation. Granzyme D was released into the medium in response to mast cell activation. Granzyme D induction was dependent on protein kinase C and nuclear factor of activated T cells (NFAT). Together, these findings identify granzyme D as a novel murine mast cell protease and implicate granzyme D in settings where mast cells are activated, such as bacterial infection and allergy.