The combination of gefitinib and RAD001 inhibits growth of HER2 overexpressing breast cancer cells and tumors irrespective of trastuzumab sensitivity

<p>Abstract</p> <p>Background</p> <p>HER2-positive breast cancers exhibit high rates of innate and acquired resistance to trastuzumab (TZ), a HER2-directed antibody used as a first line treatment for this disease. TZ resistance may in part be mediated by frequent co-expression of EGFR and by sustained activation of the mammalian target of rapamycin (mTOR) pathway. Here, we assessed feasibility of combining the EGFR inhibitor gefitinib and the mTOR inhibitor everolimus (RAD001) for treating HER2 overexpressing breast cancers with different sensitivity to TZ.</p> <p>Methods</p> <p>The gefitinib and RAD001 combination was broadly evaluated in TZ sensitive (SKBR3 and MCF7-HER2) and TZ resistant (JIMT-1) breast cancer models. The effects on cell growth were measured in cell based assays using the fixed molar ratio design and the median effect principle. <it>In vivo </it>studies were performed in Rag2M mice bearing established tumors. Analysis of cell cycle, changes in targeted signaling pathways and tumor characteristics were conducted to assess gefitinib and RAD001 interactions.</p> <p>Results</p> <p>The gefitinib and RAD001 combination inhibited cell growth <it>in vitro </it>in a synergistic fashion as defined by the Chou and Talalay median effect principle and increased tumor xenograft growth delay. The improvement in therapeutic efficacy by the combination was associated <it>in vitro </it>with cell line dependent increases in cytotoxicity and cytostasis while treatment <it>in vivo </it>promoted cytostasis. The most striking and consistent therapeutic effect of the combination was increased inhibition of the mTOR pathway (<it>in vitro </it>and <it>in vivo</it>) and EGFR signaling <it>in vivo </it>relative to the single drugs.</p> <p>Conclusions</p> <p>The gefitinib and RAD001 combination provides effective control over growth of HER2 overexpressing cells and tumors irrespective of the TZ sensitivity status.</p

Mannose-binding lectin-deficient genotypes as a risk factor of pneumococcal meningitis in infants

OBJECTIVES: The objective of this study was to evaluate to evaluate the role of mannose-binding-lectin deficient genotypes in pneumococcal meningitis (PM) in children. METHODS: We performed a 16-year retrospective study (January 2001 to March 2016) including patients ≤ 18 years with PM. Variables including attack rate of pneumococcal serotype (high or low invasive capacity) and MBL2 genotypes associated with low serum MBL levels were recorded. RESULTS: Forty-eight patients were included in the study. Median age was 18.5 months and 17/48 episodes (35.4%) occurred in children ≤ 12 months old. Serotypes with high-invasive disease potential were identified in 15/48 episodes (31.2%). MBL2 deficient genotypes accounted for 18.8% (9/48). Children ≤ 12 months old had a 7-fold risk (95% CI: 1.6-29.9; p 12 months old. A sub-analysis of patients by age group revealed significant proportions of carriers of MBL2 deficient genotypes among those ≤ 12 months old with PM caused by opportunistic serotypes (54.5%), admitted to the PICU (Pediatric Intensive Care Unit) (46.7%) and of White ethnicity (35.7%). These proportions were significantly higher than in older children (all p<0.05). CONCLUSIONS: Our results suggest that differences in MBL2 genotype in children ≤12 months old affects susceptibility to PM, and it may have an important role in the episodes caused by non-high invasive disease potential serotypes

Predicting drug pharmacokinetics and effect in vascularized tumors using computer simulation

In this paper, we investigate the pharmacokinetics and effect of doxorubicin and cisplatin in vascularized tumors through two-dimensional simulations. We take into account especially vascular and morphological heterogeneity as well as cellular and lesion-level pharmacokinetic determinants like P-glycoprotein (Pgp) efflux and cell density. To do this we construct a multi-compartment PKPD model calibrated from published experimental data and simulate 2-h bolus administrations followed by 18-h drug washout. Our results show that lesion-scale drug and nutrient distribution may significantly impact therapeutic efficacy and should be considered as carefully as genetic determinants modulating, for example, the production of multidrug-resistance protein or topoisomerase II. We visualize and rigorously quantify distributions of nutrient, drug, and resulting cell inhibition. A main result is the existence of significant heterogeneity in all three, yielding poor inhibition in a large fraction of the lesion, and commensurately increased serum drug concentration necessary for an average 50% inhibition throughout the lesion (the IC50 concentration). For doxorubicin the effect of hypoxia and hypoglycemia (“nutrient effect”) is isolated and shown to further increase cell inhibition heterogeneity and double the IC50, both undesirable. We also show how the therapeutic effectiveness of doxorubicin penetration therapy depends upon other determinants affecting drug distribution, such as cellular efflux and density, offering some insight into the conditions under which otherwise promising therapies may fail and, more importantly, when they will succeed. Cisplatin is used as a contrast to doxorubicin since both published experimental data and our simulations indicate its lesion distribution is more uniform than that of doxorubicin. Because of this some of the complexity in predicting its therapeutic efficacy is mitigated. Using this advantage, we show results suggesting that in vitro monolayer assays using this drug may more accurately predict in vivo performance than for drugs like doxorubicin. The nonlinear interaction among various determinants representing cell and lesion phenotype as well as therapeutic strategies is a unifying theme of our results. Throughout it can be appreciated that macroscopic environmental conditions, notably drug and nutrient distributions, give rise to considerable variation in lesion response, hence clinical resistance. Moreover, the synergy or antagonism of combined therapeutic strategies depends heavily upon this environment

A Systems Approach for Tumor Pharmacokinetics

Recent advances in genome inspired target discovery, small molecule screens, development of biological and nanotechnology have led to the introduction of a myriad of new differently sized agents into the clinic. The differences in small and large molecule delivery are becoming increasingly important in combination therapies as well as the use of drugs that modify the physiology of tumors such as anti-angiogenic treatment. The complexity of targeting has led to the development of mathematical models to facilitate understanding, but unfortunately, these studies are often only applicable to a particular molecule, making pharmacokinetic comparisons difficult. Here we develop and describe a framework for categorizing primary pharmacokinetics of drugs in tumors. For modeling purposes, we define drugs not by their mechanism of action but rather their rate-limiting step of delivery. Our simulations account for variations in perfusion, vascularization, interstitial transport, and non-linear local binding and metabolism. Based on a comparison of the fundamental rates determining uptake, drugs were classified into four categories depending on whether uptake is limited by blood flow, extravasation, interstitial diffusion, or local binding and metabolism. Simulations comparing small molecule versus macromolecular drugs show a sharp difference in distribution, which has implications for multi-drug therapies. The tissue-level distribution differs widely in tumors for small molecules versus macromolecular biologic drugs, and this should be considered in the design of agents and treatments. An example using antibodies in mouse xenografts illustrates the different in vivo behavior. This type of transport analysis can be used to aid in model development, experimental data analysis, and imaging and therapeutic agent design.National Institutes of Health (U.S.) (grant T32 CA079443