Allogeneic donor T cells in bone marrow transplantation (BMT) can contribute to beneficial graft-versus-leukemia (GVL) effects but can also cause detrimental graft-versus-host disease (GVHD). A successful method for the ex vivo treatment of donor T cells to limit their GVHD potential while retaining GVL activity would have broad clinical applications for patients undergoing allogeneic hematopoietic cell transplantation for malignant diseases. We hypothesized that donor lymphocyte infusions treated with fludarabine, an immunosuppressive nucleoside analog, would have reduced GVHD potential in a fully major histocompatibility complex-mismatched C57BL/6 --> B10.BR mouse BMT model. Recipients of fludarabine-treated donor lymphocyte infusions (F-DLI) had significantly reduced GVHD mortality, reduced histopathologic evidence of GVHD, and lower inflammatory serum cytokine levels than recipients of untreated DLI. Combined comparisons of GVHD incidence and donor-derived hematopoietic chimerism indicated that F-DLI had a therapeutic index superior to that of untreated DLI. Furthermore, adoptive immunotherapy of lymphoblastic lymphoma using F-DLI in the C57BL/6 --> B10.BR model demonstrated a broad therapeutic index with markedly reduced GVHD activity and preservation of GVL activity compared with untreated allogeneic T cells. Fludarabine exposure markedly reduced the CD4+CD44(low)-naive donor T-cell population within 48 hours of transplantation and altered the relative representation of cytokine-producing CD4+ T cells, consistent with T-helper type 2 polarization. However, proliferation of fludarabine-treated T cells in allogeneic recipient spleens was equivalent to that of untreated T cells. The results suggest that fludarabine reduces the GVHD potential of donor lymphocytes through effects on a CD4+CD44(low) T-cell population, with less effect on alloreactive T cells and CD4+CD44(high) memory T cells that are able to mediate GVL effects. Thus, F-DLI represents a novel method of immune modulation that may be useful to enhance immune reconstitution among allograft recipients with reduced risk of GVHD while retaining beneficial GVL effects.

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Abstract. Pharmaceutical manufacturing processes consist of a series of stages (e.g., reaction, workup, isolation) to generate the active pharmaceutical ingredient (API). Outputs at intermediate stages (inprocess control) and API need to be controlled within acceptance criteria to assure final drug product quality. In this paper, two methods based on tolerance interval to derive such acceptance criteria will be evaluated. The first method is serial worst case (SWC), an industry risk minimization strategy, wherein input materials and process parameters of a stage are fixed at their worst-case settings to calculate the maximum level expected from the stage. This maximum output then becomes input to the next stage wherein process parameters are again fixed at worst-case setting. The procedure is serially repeated throughout the process until the final stage. The calculated limits using SWC can be artificially high and may not reflect the actual process performance. The second method is the variation transmission (VT) using autoregressive model, wherein variation transmitted up to a stage is estimated by accounting for the recursive structure of the errors at each stage. Computer simulations at varying extent of variation transmission and process stage variability are performed. For the scenarios tested, VT method is demonstrated to better maintain the simulated confidence level and more precisely estimate the true proportion parameter than SWC. Real data examples are also presented that corroborate the findings from the simulation. Overall, VT is recommended for setting acceptance criteria in a multi-staged pharmaceutical manufacturing process.

Assessment of analytical similarity of tier 1 quality attributes is based on a set of hypotheses that tests the mean difference of reference and test products against a margin adjusted for standard deviation of the reference product. Thus, proper assessment of the biosimilarity hypothesis requires statistical tests that account for the uncertainty associated with the estimations of the mean differences and the standard deviation of the reference product. Recently, a linear reformulation of the biosimilarity hypothesis has been proposed, which facilitates development and implementation of statistical tests. These statistical tests account for the uncertainty in the estimation process of all the unknown parameters. In this paper, we survey methods for constructing confidence intervals for testing the linearized reformulation of the biosimilarity hypothesis and also compare the performance of the methods. We discuss test procedures using confidence intervals to make possible comparison among recently developed methods as well as other previously developed methods that have not been applied for demonstrating analytical similarity. A computer simulation study was conducted to compare the performance of the methods based on the ability to maintain the test size and power, as well as computational complexity. We demonstrate the methods using two example applications. At the end, we make recommendations concerning the use of the methods. KEYWORDSbiosimilarity, confidence interval, exact-based intervals, generalized confidence intervals, Howe's method, Wald-type confidence intervals 316 317 have no clinical impact. If this information is unavailable, the Food and Drug Administration (FDA) recommends using = f × R , where the constant f is a fixed multiplier, and R is the variability of RP. The value of f = 1.5 is justified using power-based calculations as rationalized in Tsong et al 1 and Chow. 2 In this case, the set of hypotheses in Equation 1 becomes:( 2) Currently, analytical similarity is demonstrated using a two one-sided tests (TOST) of the hypotheses in Equation 2. The TOST is conducted by computing a 100(1 − 2 )% confidence interval on the difference T − R , and equivalence is demonstrated if the entire confidence interval falls within the range from −1.5 R to 1.5 R . In this setting, is the test size. However, in practice, R is unknown and is replaced in the EAC with a sample estimate. This approach does not account for the uncertainty in the estimation of R and is not recommended because it inflates the type I error and reduces the powers of the TOST. 3 Alternative approaches have been explored to address this issue.Two reformulations of the set of hypotheses in Equation 2 that facilitate development and implementation of statistical procedures to account for the uncertainty in the estimations of all the parameters, and in particular R , have been recently proposed in the literature. Burdick et al 4 reformulated the set of hypotheses in Equation 2 as effect size by dividing both sides of the inequalities by R...

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