Agent-based modeling: a systematic assessment of use cases and requirements for enhancing pharmaceutical research and development productivity.

A crisis continues to brew within the pharmaceutical research and development (R&D) enterprise: productivity continues declining as costs rise, despite ongoing, often dramatic scientific and technical advances. To reverse this trend, we offer various suggestions for both the expansion and broader adoption of modeling and simulation (M&S) methods. We suggest strategies and scenarios intended to enable new M&S use cases that directly engage R&D knowledge generation and build actionable mechanistic insight, thereby opening the door to enhanced productivity. What M&S requirements must be satisfied to access and open the door, and begin reversing the productivity decline? Can current methods and tools fulfill the requirements, or are new methods necessary? We draw on the relevant, recent literature to provide and explore answers. In so doing, we identify essential, key roles for agent-based and other methods. We assemble a list of requirements necessary for M&S to meet the diverse needs distilled from a collection of research, review, and opinion articles. We argue that to realize its full potential, M&S should be actualized within a larger information technology framework--a dynamic knowledge repository--wherein models of various types execute, evolve, and increase in accuracy over time. We offer some details of the issues that must be addressed for such a repository to accrue the capabilities needed to reverse the productivity decline

Caseous Lymphadenitis Management in Goats

This fact sheet describes Caseous Lymphadenitis, an infectious disease of sheep and goats and gives information on managing the disease

Predicting early risk of chronic kidney disease in cats using routine clinical laboratory tests and machine learning.

BackgroundAdvanced machine learning methods combined with large sets of health screening data provide opportunities for diagnostic value in human and veterinary medicine.Hypothesis/objectivesTo derive a model to predict the risk of cats developing chronic kidney disease (CKD) using data from electronic health records (EHRs) collected during routine veterinary practice.AnimalsA total of 106 251 cats that attended Banfield Pet Hospitals between January 1, 1995, and December 31, 2017.MethodsLongitudinal EHRs from Banfield Pet Hospitals were extracted and randomly split into 2 parts. The first 67% of the data were used to build a prediction model, which included feature selection and identification of the optimal neural network type and architecture. The remaining unseen EHRs were used to evaluate the model performance.ResultsThe final model was a recurrent neural network (RNN) with 4 features (creatinine, blood urea nitrogen, urine specific gravity, and age). When predicting CKD near the point of diagnosis, the model displayed a sensitivity of 90.7% and a specificity of 98.9%. Model sensitivity decreased when predicting the risk of CKD with a longer horizon, having 63.0% sensitivity 1 year before diagnosis and 44.2% 2 years before diagnosis, but with specificity remaining around 99%.Conclusions and clinical importanceThe use of models based on machine learning can support veterinary decision making by improving early identification of CKD

High School Principals’ Leadership Roles and Use of Time

Time is the most valuable currency in schools. This study describes how successful high school principals reported spending and allocating their time to various leadership tasks using selected items from a study of principal-time use and school effectiveness in Miami-Dade County Public Schools in Florida US. Public high school principals were selected based on their 2019 Missouri School Improvement Program performance indicators and asked to complete either a paper or online survey. Findings show that principals spend more time on management-related activities than curriculum and instruction-related activities. There is a strong association between time-use on curriculum and instruction with both gender and school size. The more leadership experience a principal has, the less time they are likely to spend on school management activities. Time-use on organization management is strongly associated with school size. If instructional leadership is a fundamental priority for principals, then development and support of successful principalship will require a redesign of their roles to free time for instructional leadership. School districts that hire principals from a pool of assistant principals (AP) or invest in principal pipelines, need to develop a strategy to build APs’ instructional and leadership skills. Keywords: High school principals, principals’ time-use, principals’ roles, successful principals, instructional leadership. DOI: 10.7176/JEP/11-18-01 Publication date:June 30th 202