Olaparib and celarasertib (AZD6738) in patients with triple negative advanced breast cancer: results from Cohort E of the plasmaMATCH trial (CRUK/15/010)

Background Approximately 10-15% of triple negative breast cancers (TNBCs) have deleterious mutations in BRCA1 and BRCA2 and may benefit from polyadenosine 5’diphosphoribose polymerase (PARP) inhibitor treatment. PARP inhibitors may also increase exogenous replication stress and thereby increase sensitivity to inhibitors of ataxia telangiectasia and Rad3-related protein (ATR). This phase II study examined the activity of the combination of PARP inhibitor, Olaparib, and ATR inhibitor, celerasertib (AZD6738), in patients with advanced TNBC. Patients and methods Patients with TNBC on most recent biopsy who had received 1 or 2 lines of chemotherapy for advanced disease or had relapsed within 12 months of (neo)adjuvant chemotherapy were eligible. Treatment was olaparib 300mg twice a day continuously and celarasertib 160mg on days 1–7 on a 28 day cycle until disease progression. The primary endpoint was confirmed objective response rate (ORR). Tissue and plasma biomarker analyses were pre-planned to identify predictors of response. Results 70 evaluable patients were enrolled. Germline BRCA1/2 mutations were present in 10 (14%) patients and 3 (4%) patients had somatic BRCA mutations. The confirmed ORR was 12/70; 17.1% (95%CI: 10.4-25.5). Responses were observed in patients without germline or somatic BRCA1/2 mutations, including patients with mutations in other homologous recombination repair genes and tumours with functional homologous recombination deficiency by RAD51 foci. Conclusion The response rate to olaparib and ceralasertib did not meet pre-specified criteria for activity in the overall evaluable population, but responses were observed in patients who would not be expected to respond to Olaparib monotherapy

Çok sınıflı, baz-stok denetimindeki sistemlerin dinamik çizelgemesi için servis modelleri

This study is on the service models for dynamic scheduling of multi-class make-to-stock systems. An exponential single-server facility processes different types of items one by one and demand arrivals for different item types occur according to independent Poisson processes. Inventories of the items are managed by base-stock policies and backordering is allowed. The objective is to minimize base-stock investments or average inventory holding costs subject to a constraint on the aggregate fill rate, which is a weighted average of the fill rates of the item types. The base-stock controlled policy that maximizes aggregate fill rate is numerically investigated, for both symmetric and asymmetric systems, and is shown to be optimal for minimizing base-stock investments under an aggregate fill rate constraint. Alternative policies are generated by heuristics in order to approximate the policy that maximizes aggregate fill rate and performances of these policies are compared to those of two well-known Longest Queue and First Come First Served policies. Also, optimal policy for the service model to minimize average inventory holding cost subject to an aggregate fill rate constraint is investigated without restricting the attention to only base-stock controlled dynamic scheduling policies. Based on the equivalence relations between this service model and the corresponding cost model, it is observed that the base-stock controlled policy that maximizes aggregate fill rate is almost the same as the solution to the service model and cost model under consideration, especially when backorder penalties are large in the cost model as compared to cost parameters for inventory holding or equivalently when the target fill rate is large in the service model.M.S. - Master of Scienc

Enerji politikaları analizi için matematiksel modelleme.

As is now generally accepted, climate change and environmental degradation has largely been triggered by carbon emissions and energy modeling for policy analysis has therefore attained renewed urgency. It is important for governments to satisfy emission targets and timetables set down by international agreements without disregarding macroeconomic concerns and restrictions. In this study, we present a large-scale nonlinear optimization model that allows the analysis of macroeconomic and multi-sectoral energy policies in respect of technological and environmental options and scenarios. The model consists of a detailed representation of energy activities and disaggregates the rest of the economy into five main sectors. Economy-wide solutions are obtained by computing a utility maximizing aggregate consumption bundle on the part of a representative household. Intersectoral and foreign transaction balances are maintained using a modified accounting matrix. The model also computes the impact on macroeconomic variables of greenhouse gas (GHG) emission strategies and abatement schemes. As such the model is capable of producing solutions that can be used to benchmark regulatory instruments and policies. Several scenarios are presented for the case of Turkey in which the impact of a nuclear power programme and power generation coupled with carbon-capture-and-storage schemes are investigated as well as setting quotas on total and sectoral GHG emissions.Ph.D. - Doctoral Progra

Heuristics for Dynamic Scheduling of Multi-Class Base-Stock Controlled Systems

Dynamic scheduling of an exponential single-server facility processing different types of items one by one is studied for the case of Poisson demand arrivals. Inventories of the items are managed by base-stock policies and backordering is allowed. Structure of the optimal scheduling policy is investigated numerically with respect to a weighted average of the fill rates. Performance of the optimal policy is compared to those of two well-known policies, Longest Queue and First-Come-First-Served, and alternative policies are generated by heuristics in order to approximate the optimal policy

Using aggregate fill rate for dynamic scheduling of multi-class systems

For dynamic scheduling of multi-class systems where backorder cost is incurred per unit backordered regardless of the time needed to satisfy backordered demand, the following models are considered: the cost model to minimize the sum of expected average inventory holding and backorder costs and the service model to minimize expected average inventory holding cost under an aggregate fill rate constraint. Use of aggregate fill rate constraint in the service model instead of an individual fill rate constraint for each class is justified by deriving equivalence relations between the considered cost and service models. Based on the numerical investigation that the optimal policy for the cost model is a base-stock policy with switching curves and fixed base-stock levels, an alternative service model is considered over the class of base-stock controlled dynamic scheduling policies to minimize the total inventory (base-stock) investment under an aggregate fill rate constraint. The policy that solves this alternative model is proposed as an approximation of the optimal policy of the original cost and the equivalent service models. Very accurate heuristics are devised to approximate the proposed policy for given base-stock levels. Comparison with base-stock controlled First Come First Served (FCFS) and Longest Queue (LQ) policies and an extension of LQ policy (Delta policy) shows that the proposed policy performs much better to solve the service models under consideration, especially when the traffic intensity is high

Turkish Energy Sector Development and the Paris Agreement Goals: A CGE Model Assessment

In the 2015 Paris Agreement, Turkey pledged to reduce greenhouse gas (GHG) emissions by 21% by 2030 relative to business-as-usual (BAU). However, Turkey currently relies heavily on imported energy and fossil-intensive power generation. Despite significant wind and solar energy potential, only 5.1% of its total power is generated by wind and solar installations; additionally, although two nuclear power stations are planned, no nuclear capacity currently exists. We expect that fulfilling Turkey’s Paris Agreement pledge will likely require a reduced reliance on fossil-based energy and additional investments in low-carbon energy sources, which may impact Turkey’s GDP, energy use, and electricity generation profiles. To fully assess these impacts, we develop a computable general equilibrium (CGE) model of the Turkish economy that combines macroeconomic representation of non-electric sectors with a detailed representation of the electricity sector. We analyze several scenarios to assess the impact of an emission trading scheme in Turkey: one including the planned nuclear development and a renewable subsidy scheme (BAU), and in the other with no nuclear technology allowed (NoN). Our assessment shows that in 2030, without policy, primary energy will be mainly oil, natural gas and coal. Under an emission trading scheme, however, coal-fired power generation vanishes by 2030 in both BAU and NoN due to the high cost of carbon. With nuclear (BAU), GHG emissions are 3.1% lower than NoN due to the resulting energy mix, allowing for a lower carbon price ($50/tCO2 in BAU compared to$70/tCO2 in NoN). Our results suggest that fulfillment of Turkey’s Paris Agreement pledge may be possible at a modest economic cost of about 0.8–1% by 2030.Bora Kat’s research activities at MIT Joint Program on the Science and Policy of Global Change were funded by the Turkish Fulbright Commission under Visiting Scholar Program. The MIT Joint Program is supported by an international consortium of government, industry and foundation sponsors (see the list at: https://globalchange.mit.edu/sponsors)