E. Espinosa Arranz

Oxycodone: a pharmacological and clinical review

Oxycodone is a semi-synthetic opioid with an agonist activity on mu, kappa and delta receptors. Equivalence with regard to morphine is 1:2. Its effect commences one hour after administration and lasts for 12 h in the controlled-release formulation. Plasma half-life is 3–5 h (half that of morphine) and stable plasma levels are reached within 24 h (2–7 days for morphine). Oral bioavailability ranges from 60 to 87%, and plasma protein binding is 45%. Most of the drug is metabolised in the liver, while the rest is excreted by the kidney along with its metabolites. The two main metabolites are oxymorphone — which is also a very potent analgesic — and noroxycodone, a weak analgesic. Oxycodone metabolism is more predictable than that of morphine, and therefore titration is easier. Oxycodone has the same mechanism of action as other opioids: binding to a receptor, inhibition of adenylyl-cyclase and hyperpolarisation of neurons, and decreased excitability. These mechanisms also play a part in the onset of dependence and tolerance. The clinical efficacy of oxycodone is similar to that of morphine, with a ratio of 1/1.5–2 for the treatment of cancer pain. Long-term administration may be associated with less toxicity in comparison with morphine. In the future, both opioids could be used simultaneously at low doses to reduce toxicity. It does not appear that there are any differences between immediate and slow-release oxycodone, except their half-life is 3–4 h, and 12 h, respectively. In Spain, controlled-release oxycodone (OxyContin®) is marketed as 10-, 20-, 40-or 80-mg tablets for b.i.d. administration. Tablets must be taken whole and must not be broken, chewed or crushed. There is no food interference. The initial dose is 10 mg b.i.d. for new treatments and no dose reduction is needed in the elderly or in cases of moderate hepatic or renal failure. Immediate-release oxycodone (OxyNorm®) is also available in capsules and oral solution. Side effects are those common to opioids: mainly nausea, constipation and drowsiness. Vomiting, pruritus and dizziness are less common. The intensity of these side effects tends to decrease over the course of time. Oxycodone causes somewhat less nausea, hallucinations and pruritus than morphine.

Cutaneous malignant melanoma and sun exposure in Spain.

Cutaneous malignant melanoma has an increasing importance all over the world. However very few epidemiological studies have been published from Spain, and Spanish people have not become aware of the problem. This study was designed to examine sun exposure patterns and other related items among 116 consecutive patients with melanoma and 235 controls. Each subject answered a questionnaire covering the place of residence, sun exposure details and other risk factors, and underwent a skin examination. Continuous sun exposure due to residence or occupation was associated with an odds ratio (OR) of 2.0 (95% confidence interval [CI] = 1.2-3.3). People who lived in the city but spent 50% of their time in rural areas for holidays had an OR of 2.2 (95% CI = 1.3-3.8) when compared with those living in urban and rural areas. The OR for people who sunbathed more than 30 times a year was 1.8 (95% CI = 1.2-2.8), and outdoor leisure time was also associated with melanoma appearance when exposure was greater than 60 units in the last 2 years, with an OR of 3.0 (95% CI = 1.6-5.5); 1 unit is equivalent to total body sun exposure for at least 2 h. These OR estimates were adjusted for age, skin type and the number of naevi. Construction workers (OR = 1.6; 95% CI = 0.5-5.6) had increased risk after adjustment for skin type, age and freckle count (OR = 4.3; 95% CI = 1.8 9.9) or mole count (OR = 2.8; 95% CI = 1.4-5.8). Working as a farmer was a protective factor after adjustment (OR = 0.5; 95% CI = 0.3-0.8). The use of sunscreens was a protective factor against melanoma (OR = 2.6; 95% CI = 1.6-3.6 for non-users). Campaigns should focus on advising people to avoid sun exposure in sunny places and to use sunscreens every time they are exposed to the sun.

Abstract P1-02-06: Computational modeling predicts drugs response to targeting metabolism in breast cancer cells

Background Reprogramming of metabolism is a hallmark in cancer. In previous works we observed differences in glucose metabolism between tumors from different breast cancer subtypes, suggesting the possibility to use drugs against metabolism in this disease. Flux Balance Analysis (FBA) is widely used to study biochemical networks, allowing to predict growth rates and to simulate drug response. Material and methods Breast cancer cell lines and different drugs against metabolic targets were evaluated with dose-response curves, and pharmacological parameters for each condition were calculated. Proteomics data from breast cancer cells lines treated with sub-lethal doses and controls were obtained applying a mass spectrometry-based approach. Differences in protein expression between treated vs. control were assessed. An FBA approach using the human metabolic reconstruction Recon2 and including the protein expression values from perturbation experiments was also applied. Model predictions were validated using dynamic FBA and growth rate for each sample was estimated. With the aim to compare the activity of the different pathway fluxes between control and treated cells, flux activity was calculated for each condition and for each pathway and response predictive models were performed. Results Drug response was diverse across different breast cancer cells. Mass spectrometry from cell samples allows identifying and quantifying 4,114 proteins. FBA predicted that growth rates decrease in treated cells vs. control, as observed in cell viability assays. Dynamic FBA showed that our model correctly reflects cell growth rates. Finally, using flux activities, it is possible to build models which could predict response against these drugs. Conclusions Proteomics provide insights of the mechanisms responsible of cells9 response to metabolism drugs. A validated computational model able to predict tumor growth using data from proteomics was developed. Model predicts growth rates and also dysregulation of biological processes triggered by drug treatment. Moreover, these computational approaches could be used to propose new mechanisms of action and effects of metabolic drugs. Acknowledgments This work was supported by grant PI15/01310 from Instituto de Salud Carlos III, Spanish Economy and Competitiveness Ministry, Spain and co-funded by FEDER program, “Una forma de hacer Europa”. L.T.-F is supported by Spanish Economy and Competitiveness Ministry grant DI-15-07614. Competing interest JAFV, AG-P and EE are stakeholders of Biomedica Molecular Medicine S.L. and Biomedica Molecular Medicine Ltd. LT-F is an employee of Biomedica Molecular Medicine S.L. The authors have declared no other conflict of interest. Citation Format: Zamora Aunon P, Trilla-Fuertes L, Diaz-Almiron M, Gamez-Pozo A, Prado-Vazquez G, Zapater-Moros A, Llorente-Armijo S, Gaya Romero F, Espinosa Arranz E, Fresno-Vara JA. Computational modeling predicts drugs response to targeting metabolism in breast cancer cells [abstract]. In: Proceedings of the 2017 San Antonio Breast Cancer Symposium; 2017 Dec 5-9; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2018;78(4 Suppl):Abstract nr P1-02-06.