The determination for early cleavage-stage embryos of noninvasive morphologic and metabolic criteria that are predictive of blastocyst development and/or full-term viability remains an important research target. We describe the derivation of a logistic regression model that predicts the probability of porcine blastocyst formation in vitro. Pig zygotes, derived by in vitro maturation and fertilization of slaughterhouse oocytes, were cultured in NCSU-23 medium that was supplemented with a mixture of 20 amino acids (NCSU-23(aa)). On Day 1, at 21, 23, 25, 27, 29 and 31 h postinsemination, cleaving embryos were evaluated morphologically in terms of the: i) number of blastomeres, ii) evenness of division, and iii) degree of fragmentation. These embryos were then placed in 1.5-microl drops of NCSU-23(aa) for 24 h, after which time the three morphologic criteria were re-evaluated and 1.2 microl of spent medium were removed for analysis by HPLC, in order to determine the net rates of amino acid depletion and appearance. Embryos were then cultured singly in NCSU-23(aa) by placing them between the filaments of a woven polyester mesh until Day 6, in order to permit the identification of individual embryos. Of 256 cleaved embryos, 28.7 +/- 6.2% (n = 5 replicates) developed into blastocysts. Discriminant analysis was used to select a subset of amino acids (threonine, valine, lysine, and phenylalanine) that discriminated optimally between embryos that became blastocysts or degenerated. These discriminant scores were entered into the logistic regression. Significant univariate relationships were established between the probability of blastocyst development and amino acid score (odds ratio [OR] 0.53, 95% confidence interval [CI] 0.40-0.69, P < 0.001), cleavage time (OR 0.79, 95% CI 0.71-0.87, P < 0.001), degree of fragmentation on Day 1 (OR 0.55, 95% CI 0.35-0.84, P = 0.009) and Day 2 (OR 0.53, 95% CI 0.35-0.78, P = 0.002), evenness of division on Day 2 (OR 0.66, 95% CI 0.46-0.96, P = 0.028), and categorical values of blastomere number on Day 2 (all P < 0.02), although no single variate could accurately predict blastocyst formation. However, multivariate analysis of the cell numbers on Day 1 and Day 2 correctly classified 51.9% of the predicted blastocysts. The inclusion of cleavage time in the regression analysis raised this rate to 63.5%, which was increased to 66.2% by the addition of evenness of division and degree of fragmentation. Finally, the full logistic regression model, which incorporated amino acid score together with all the other morphologic and kinetic variables, correctly classified 80.8% of the predicted blastocysts. This represented 51.2% of the observed blastocysts. Our data are novel in that they not only define in a quantitative manner the influence of previously undescribed predictors of porcine blastocyst formation, but they also provide a simple model of preimplantation development with reasonable predictive accuracy. The present study also provides a basic model for the examination and incorporation of additional early morphologic and metabolic correlates of developmental competence and could potentially be applied to the selection of human embryos for transfer in clinical IVF.