Publications

Abstract Introduction In 2003, Purdue University College of Pharmacy (PUCOP) in West Lafayette, Indiana, began the Purdue Kenya Partnership (PKP) in collaboration with the Academic Model Providing Access to Healthcare, Moi University, and Moi Teaching and Referral Hospital, in Eldoret, Kenya. PUCOP's involvement utilized a tripartite approach of engagement, education, and scholarship to provide and expand sustainable access to high quality care. Objective This paper discusses outcomes and impacts of this academic partnership. Methods Purdue Kenya Partnership's progress in achieving its stated mission was evaluated using an outcome-approach logic model. This model highlighted inputs, activities, and results which encompassed outputs, outcomes, and impact. A comprehensive set of ratios were calculated to quantify annual change in PKP investments against estimated metrics for engagement, education, and scholarship. These metrics were weighted by involvement level and pharmacist effort in various clinical domains. Descriptive statistics were completed that identified cumulative and totals per year for each collected data type of data collected. Results Purdue Kenya Partnership implementation utilized initial inputs of human resources, financial capital, and strategic partnerships. These inputs supported pharmacy involvement in 16 distinct care programs in both inpatient and outpatient settings which supported the care of 457 833 individual patients and grown a clinical pharmacy staff from 0 to 22 practicing clinical pharmacists. Five unique educational programs have been established which have graduated 457 trainees. Purdue Kenya Partnership has generated over \$6.2 million in grant funding and disseminated 302 peer reviewed manuscripts, posters, and oral presentations combined. Ratios describing trends in engagement, education, and scholarship as a result of using the locally focused PKP approach highlight higher initial costs compared with much lower costs per outcome several years into the partnership. Conclusion The PKP's global health approach of prioritizing the population's care needs (“leading with care”) has enabled the development of sustainable engagement, education, and scholarship infrastructure with significant gains in all three domains.

The Poisson–Boltzmann equation (PBE) is a nonlinear elliptic parametrized partial differential equation that arises in biomolecular modeling and is a fundamental tool for structural biology. It is used to calculate electrostatic potentials around an ensemble of fixed charges immersed in an ionic solution. Efficient numerical computation of the PBE yields a high number of degrees of freedom in the resultant algebraic system of equations, ranging from several hundred thousand to millions. Coupled with the fact that in most cases the PBE requires to be solved multiple times for a large number of system configurations, for example, in Brownian dynamics simulations or in the computation of similarity indices for protein interaction analysis, this poses great computational challenges to conventional numerical techniques. To accelerate such onerous computations, we suggest to apply the reduced basis method (RBM) and the (discrete) empirical interpolation method ((D)EIM) to the PBE with a special focus on simulations of complex biomolecular systems, which greatly reduces this computational complexity by constructing a reduced order model (ROM) of typically low dimension. In this study, we employ a simple version of the PBE for proof of concept and discretize the linearized PBE (LPBE) with a centered finite difference scheme. The resultant linear system is solved by the aggregation-based algebraic multigrid (AGMG) method at different samples of ionic strength on a three-dimensional Cartesian grid. The discretized LPBE, which we call the high-fidelity full order model (FOM), yields solution as accurate as other LPBE solvers. We then apply the RBM to the FOM. DEIM is applied to the Dirichlet boundary conditions which are nonaffine in the parameter (ionic strength), to reduce the complexity of the ROM. From the numerical results, we notice that the RBM reduces the model order from $${\mathcal {N}} = 2\times 10^{6}$$to $$N = 6$$at an accuracy of $${\mathcal {O}}(10^{-9})$$and reduces the runtime by a factor of approximately 7600. DEIM, on the other hand, is also used in the offline-online phase of solving the ROM for different values of parameters which provides a speed-up of 20 for a single iteration of the greedy algorithm.