NONMEM version III implementation on a VAX 9000: a DCL procedure for single-step execution and the unrealized advantage of a vectorizing FORTRAN compiler.
There is great interest within the FDA, academia, and the pharmaceutical industry to provide more detailed information about the time course of drug concentration and effect in subjects receiving a drug as part of their overall therapy. Advocates of this effort expect the eventual goal of these endeavors to provide labeling which reflects the experience of drug administration to the entire population of potential recipients. The set of techniques which have been thus far applied to this task has been defined as population approach methodologies. While a consensus view on the usefulness of these techniques is not likely to be formed in the near future, most pharmaceutical companies or individuals who provide kinetic/dynamic support for drug development programs are investigating population approach methods. A major setback in this investigation has been the shortage of computational tools to analyze population data. One such algorithm, NONMEM, supplied by the NONMEM Project Group of the University of California, San Francisco has been widely used and remains the most accessible computational tool to date. The program is distributed to users as FORTRAN 77 source code with instructions for platform customization. Given the memory and compiler requirements of this algorithm and the intensive matrix manipulation required for run convergence and parameter estimation, this program's performance is largely determined by the platform and the FORTRAN compiler used to create the NONMEM executable. Benchmark testing on a VAX 9000 with Digital's FORTRAN (v. 1.2) compiler suggests that this is an acceptable platform. Due to excessive branching within the loops of the NONMEM source code, the vector processing capabilities of the KV900-AA vector processor actually decrease performance. A DCL procedure is given to provide single step execution of this algorithm.
Vielhaber
J P
JP
Rochester Institute of Technology, NY.
Kuhlman
J V
JV
Barrett
J S
JS
D000465:Algorithms; D004341:Drug Evaluation; D016018:Least-Squares Analysis; D008432:Mathematical Computing; D008954:Models, Biological; D011159:Population Surveillance; D011381:Programming Languages; D012984:Software; D012985:Software Design