A rank-based approach to improve the efficiency of inferential seamless phase 2/3 clinical trials with dose optimization.

To accelerate clinical development, seamless 2/3 adaptive design is an attractive strategy to combine phase 2 dose selection with phase 3 confirmatory objectives. As the regulatory requirement for dose optimization in oncology drugs shifted from maximum tolerated dose to maximum effective dose, it's important to gather more data on multiple candidate doses to inform dose selection. A phase 3 dose may be selected based on phase 2 results and carried forward in phase 3 study. Data obtained from both phases will be combined in the final analysis. In many disease settings biomarker endpoints are utilized for dose selection as they are correlated with the clinical efficacy endpoints. As discussed in Li et al. (2015), the combined analysis may cause type I error inflation due to the correlation and dose selection. Sidák adjustment has been proposed to control the overall type I error by adjusting p-values in phase 2 when performing the combined p-value test. However, this adjustment could be overly conservative as it does not consider the underlying correlations among doses/endpoints. We propose an alternative approach utilizing biomarker rank-based ordered test statistics which takes the rank order of the selected dose and the correlation into consideration. If the correlation is unknown, we propose a rank-based Dunnett adjustment, which includes the traditional Dunnett adjustment as a special case. We show that the proposed method controls the overall type I error, and leads to a uniformly higher power than Sidák adjustment and the traditional Dunnett adjustment under all potential correlation scenarios discussed.

An aza-nucleoside, fragment-like inhibitor of the DNA repair enzyme alkyladenine glycosylase (AAG).

The DNA repair enzyme AAG has been shown in mice to promote tissue necrosis in response to ischaemic reperfusion or treatment with alkylating agents. A chemical probe inhibitor is required for investigations of the biological mechanism causing this phenomenon and as a lead for drugs that are potentially protective against tissue damage from organ failure and transplantation, and alkylative chemotherapy. Herein, we describe the rationale behind the choice of arylmethylpyrrolidines as appropriate aza-nucleoside mimics for an inhibitor followed by their synthesis and the first use of a microplate-based assay for quantification of their inhibition of AAG. We finally report the discovery of an imidazol-4-ylmethylpyrrolidine as a fragment-sized, weak inhibitor of AAG.

Disparities in spread and control of influenza in slums of Delhi: findings from an agent-based modelling study.

OBJECTIVES: This research studies the role of slums in the spread and control of infectious diseases in the National Capital Territory of India, Delhi, using detailed social contact networks of its residents. METHODS: We use an agent-based model to study the spread of influenza in Delhi through person-to-person contact. Two different networks are used: one in which slum and non-slum regions are treated the same, and the other in which 298 slum zones are identified. In the second network, slum-specific demographics and activities are assigned to the individuals whose homes reside inside these zones. The main effects of integrating slums are that the network has more home-related contacts due to larger family sizes and more outside contacts due to more daily activities outside home. Various vaccination and social distancing interventions are applied to control the spread of influenza. RESULTS: Simulation-based results show that when slum attributes are ignored, the effectiveness of vaccination can be overestimated by 30%-55%, in terms of reducing the peak number of infections and the size of the epidemic, and in delaying the time to peak infection. The slum population sustains greater infection rates under all intervention scenarios in the network that treats slums differently. Vaccination strategy performs better than social distancing strategies in slums. CONCLUSIONS: Unique characteristics of slums play a significant role in the spread of infectious diseases. Modelling slums and estimating their impact on epidemics will help policy makers and regulators more accurately prioritise allocation of scarce medical resources and implement public health policies.

Combining Participatory Influenza Surveillance with Modeling and Forecasting: Three Alternative Approaches.

BACKGROUND: Influenza outbreaks affect millions of people every year and its surveillance is usually carried out in developed countries through a network of sentinel doctors who report the weekly number of Influenza-like Illness cases observed among the visited patients. Monitoring and forecasting the evolution of these outbreaks supports decision makers in designing effective interventions and allocating resources to mitigate their impact. OBJECTIVE: Describe the existing participatory surveillance approaches that have been used for modeling and forecasting of the seasonal influenza epidemic, and how they can help strengthen real-time epidemic science and provide a more rigorous understanding of epidemic conditions. METHODS: We describe three different participatory surveillance systems, WISDM (Widely Internet Sourced Distributed Monitoring), Influenzanet and Flu Near You (FNY), and show how modeling and simulation can be or has been combined with participatory disease surveillance to: i) measure the non-response bias in a participatory surveillance sample using WISDM; and ii) nowcast and forecast influenza activity in different parts of the world (using Influenzanet and Flu Near You). RESULTS: WISDM-based results measure the participatory and sample bias for three epidemic metrics i.e. attack rate, peak infection rate, and time-to-peak, and find the participatory bias to be the largest component of the total bias. The Influenzanet platform shows that digital participatory surveillance data combined with a realistic data-driven epidemiological model can provide both short-term and long-term forecasts of epidemic intensities, and the ground truth data lie within the 95 percent confidence intervals for most weeks. The statistical accuracy of the ensemble forecasts increase as the season progresses. The Flu Near You platform shows that participatory surveillance data provide accurate short-term flu activity forecasts and influenza activity predictions. The correlation of the HealthMap Flu Trends estimates with the observed CDC ILI rates is 0.99 for 2013-2015. Additional data sources lead to an error reduction of about 40% when compared to the estimates of the model that only incorporates CDC historical information. CONCLUSIONS: While the advantages of participatory surveillance, compared to traditional surveillance, include its timeliness, lower costs, and broader reach, it is limited by a lack of control over the characteristics of the population sample. Modeling and simulation can help overcome this limitation as well as provide real-time and long-term forecasting of influenza activity in data-poor parts of the world.

Effect of modelling slum populations on influenza spread in Delhi.

OBJECTIVES: This research studies the impact of influenza epidemic in the slum and non-slum areas of Delhi, the National Capital Territory of India, by taking proper account of slum demographics and residents' activities, using a highly resolved social contact network of the 13.8 million residents of Delhi. METHODS: An SEIR model is used to simulate the spread of influenza on two different synthetic social contact networks of Delhi, one where slums and non-slums are treated the same in terms of their demographics and daily sets of activities and the other, where slum and non-slum regions have different attributes. RESULTS: Differences between the epidemic outcomes on the two networks are large. Time-to-peak infection is overestimated by several weeks, and the cumulative infection rate and peak infection rate are underestimated by 10-50%, when slum attributes are ignored. CONCLUSIONS: Slum populations have a significant effect on influenza transmission in urban areas. Improper specification of slums in large urban regions results in underestimation of infections in the entire population and hence will lead to misguided interventions by policy planners.

A Cascade Strategy Enables a Total Synthesis of (±)-Morphine.

Morphine has been a target for synthetic chemists since Robinson proposed its correct structure in 1925, resulting in a large number of total syntheses of morphine alkaloids. Here we report a total synthesis of (±)-morphine that employs two key strategic cyclizations: 1) a diastereoselective light-mediated cyclization of an O-arylated butyrolactone to form a tricyclic cis-fused benzofuran and 2) a cascade ene-yne-ene ring closing metathesis to forge the tetracyclic morphine core. This approach enables a short and stereoselective synthesis of morphine in an overall yield of 6.6 %.

A cascade strategy enables a total synthesis of (-)-gephyrotoxin.

A concise and efficient synthesis of (-)-gephyrotoxin from L-pyroglutaminol has been realized. The key step in this approach is a diastereoselective intramolecular enamine/Michael cascade reaction that forms two rings and two stereocenters and generates a stable tricyclic iminium cation. A hydroxy-directed reduction of this intermediate plays a key role in establishing the required cis-decahydroquinoline ring system, enabling the total synthesis of (-)-gephyrotoxin in nine steps and 14% overall yield. The absolute configuration of the synthetic material was confirmed by single-crystal X-ray diffraction and is consistent with the structure originally proposed for material isolated from the natural source.