title: Determination of in-process limits during parenteral solution manufacturing using Monte Carlo Simulation.
abstract: The purpose of this study is to utilize Monte Carlo Simulation methodology to determine the in-process limits for the parenteral solution manufacturing process. The Monte Carlo Simulation predicts the distribution of a dependable variable (such as drug concentration) in a naturally occurring process through random value generation considering the variability associated with the depended variable. The propagation of variation in drug concentration from batch to batch is cascading in nature during the following four formulation steps: 1) determination of drug raw material potency (or purity), 2) weighing of drug raw material, 3) measurement of batch volume, and 4) determination of drug concentration in the mix tank. The coefficients of variation for these four steps are denoted as CV1, CV2, CV3, and CV4, respectively. The Monte Carlo Simulation was performed for each of the above four cascading steps. The results of the simulation demonstrate that the in-process limits of the drug can be successfully determined using the Monte Carlo Simulation. Once the specification limits are determined, the Monte Carlo Simulation can be used to study the effect of each variability on the percent out of specification limits (OOL) for the in-process testing. Demonstrations were performed using the acceptance criterion of less than 5% of OOL batches, and the typical values of CV2 and CV3 being equal to 0.03% and 0.5%, respectively. The results show that for the in-process limits of +/- 1%, the values of CV1 and CV4 should not be greater than 0.1%. These assay requirements appear to be difficult to achieve for a given chemical analytical method. By comparison, for the In-process limits of +/- 4%, the requirements are much easier to achieve. The values of CV1 and CV4 should not be greater than 1.38%. In addition, the relationship between the percent OOL versus CV1 or CV4 is nonlinear per se. The number of OOL batches increases rapidly with increasing variability of CV1 or CV4.
authors.affiliation: Pharmaceutical Sciences R&D/Medication Delivery Division, Baxter Healthcare Co., Round Lake, IL 60073, USA.
authors.forename: Wei Y
authors.initials: WY
authors.lastname: Kuu
authors.forename: Rao
authors.initials: R
authors.lastname: Chilamkurti
mesh_terms: D002626:Chemistry, Pharmaceutical / Q000379:methods*; D007263:Infusions, Parenteral; D009010:Monte Carlo Method; D012984:Software; D012996:Solutions
chemical_list: D012996:Solutions