PredicDiff™: a computational tool for the prediction of PERLs concentrations based on extractables data

PredicDiff™, a computational modeling tool that allows fitting diffusion curves to process equipment related leachables (PERLs) is presented. Based on the measurement of extractables (analytical data), Fick's second law of diffusion, and a Trust Region Rebounds based fitting algorithm (optimize.curve_fit from Scipy), the Python-based model fits a diffusion curve to each extractable/PERL thus allowing the determination of the PERL amount after any arbitrary contact time and temperature for example, the actual production conditions. In addition, PredicDiff™ delivers the system's diffusion- and partition coefficients and the equilibrium concentration. Three case studies are presented: 1) interpolation of ε-caprolactam from a polysulfone disconnector from 24 h to 2 h, 2) adjustment of the diffusion of ε-caprolactam from a polysulfone disconnector from 40 °C to 21 °C, and 3) extrapolation of 2,4-Di-tert-butylphenol from an ultra-low-density polyethylene (ULDPE) bag from 70 to 90 days. In addition, the usability of PredicDiff™ for inter- or extrapolation of an unidentified extractable from a silicone tubing is shown. In the first case, after a contact time of 2 h, the concentration and hence, also patient exposure to ε-caprolactam is reduced by 70 % in comparison to the extractable value after 24 h. In the second case, further adjustment based on contact temperature (21 °C vs. 40 °C) gives a total reduction of 87 %. In the third case, the concentration and therefore, also patient exposure to 2,4-Di-tert-butylphenol increases by 2.6 % if storage is prolonged from 70 days to 90 days. PredicDiff™ has no limitations on the types of extractables (including those whose identities are not elucidated) or concentration ranges. Based on the remodeling of diffusion curves from literature and the calculation of extractables amounts from studies (analytical data), it is shown that PredicDiff™ provides reliable results within an acceptable range of uncertainty. Inter- and extrapolated PERLs can support the extractables and leachables (E&L) risk management by quickly calculating a more realistic concentration and ultimately, patient exposure.