Bridging responses to a human telomerase reverse transcriptase-based peptide cancer vaccine candidate in a mechanism-based model.

Therapeutic cancer vaccines are novel immuno-therapeutics, aiming to improve clinical outcomes with other immunotherapies. However, obstacles to their successful clinical development remain, which model-informed drug development approaches may address. UV1 is a telomerase based therapeutic cancer vaccine candidate being investigated in phase I clinical trials for multiple indications. We developed a mechanism-based model structure, using a nonlinear mixed-effects modeling techniques, based on longitudinal tumor sizes (sum of the longest diameters, SLD), UV1-specific immunological assessment (stimulation index, SI) and overall survival (OS) data obtained from a UV1 phase I trial including non-small cell lung cancer (NSCLC) patients and a phase I/IIa trial including malignant melanoma (MM) patients. The final structure comprised a mechanistic tumor growth dynamics (TGD) model, a model describing the probability of observing a UV1-specific immune response (SI ≥ 3) and a time-to-event model for OS. The mechanistic TGD model accounted for the interplay between the vaccine peptides, immune system and tumor. The model-predicted UV1-specific effector CD4+ T cells induced tumor shrinkage with half-lives of 103 and 154 days in NSCLC and MM patients, respectively. The probability of observing a UV1-specific immune response was mainly driven by the model-predicted UV1-specific effector and memory CD4+ T cells. A high baseline SLD and a high relative increase from nadir were identified as main predictors for a reduced OS in NSCLC and MM patients, respectively. Our model predictions highlighted that additional maintenance doses, i.e. UV1 administration for longer periods, may result in more sustained tumor size shrinkage.

Assessment of ibrutinib scheduling on leukocyte, lymph node size and blood pressure dynamics in chronic lymphocytic leukemia through pharmacokinetic-pharmacodynamic models.

Ibrutinib is a Bruton tyrosine kinase (Btk) inhibitor for treating chronic lymphocytic leukemia (CLL). It has also been associated with hypertension. The optimal dosing schedule for mitigating this adverse effect is currently under discussion. A quantification of relationships between systemic ibrutinib exposure and efficacy (i.e., leukocyte count and sum of the product of perpendicular diameters [SPD] of lymph nodes) and hypertension toxicity (i.e., blood pressure), and their association with overall survival is needed. Here, we present a semi-mechanistic pharmacokinetic-pharmacodynamic modeling framework to characterize such relationships and facilitate dose optimization. Data from a phase Ib/II study were used, including ibrutinib plasma concentrations to derive daily 0-24-h area under the concentration-time curve, leukocyte count, SPD, survival, and blood pressure measurements. A nonlinear mixed effects modeling approach was applied, considering ibrutinib's pharmacological action and CLL cell dynamics. The final framework included (i) an integrated model for SPD and leukocytes consisting of four CLL cell subpopulations with ibrutinib inhibiting phosphorylated Btk production, (ii) a turnover model in which ibrutinib stimulates an increase in blood pressure, and (iii) a competing risk model for dropout and death. Simulations predicted that the approved dosing schedule had a slightly higher efficacy (24-month, progression-free survival [PFS] 98%) than de-escalation schedules (24-month, average PFS ≈ 97%); the latter had, on average, ≈20% lower proportions of patients with hypertension. The developed modeling framework offers an improved understanding of the relationships among ibrutinib exposure, efficacy and toxicity biomarkers. This framework can serve as a platform to assess dosing schedules in a biologically plausible manner.

Optimization of tannase production by Aspergillus glaucus in solid-state fermentation of black tea waste.

Tannases are valuable industrial enzymes used in food, pharmaceutical, cosmetic, leather manufacture and in environmental biotechnology. In this study, 15 fungal isolates were obtained from Egyptian cultivated soil and marine samples. The isolated fungi were qualitatively and quantitatively screened for their abilities to produce tannase. The selected fungal isolate NRC8 giving highest tannase activity was identified by molecular technique (18S rRNA) as Aspergillus glaucus. Among different tannin-containing wastes tested, the black tea waste was the best substrate for tannase production by Aspergillus glaucus in solid-state fermentation (SSF). Optimization of the different process parameters required for maximum enzyme production was carried out to design a suitable SSF process. Maximal tannase production was achieved with moisture content of 75%, an inoculums size of 6 × 108 spore/ml and sodium nitrate 0.2% (pH of 5.0) at 30 °C after 5 days of incubation. Box-Behnken experiment was designed to get a quadratic model for further optimization studies. Four-factor response-surface method with 27 runs was prepared using independent parameters including (moisture content %, initial pH, substrate concentration (g) and sodium nitrate concentration (g) for tannase model. The F- and P-values of the model were 4.30 and 0.002, respectively, which implied that the model is significant. In addition, the lack-of-fit was 1040.37 which indicates the same significance relative to the pure error. A. glaucus tannase was evaluated by the efficiency of conversion of tannic acid to gallic acid. Moreover, production of gallic acid from SSF process of A. glaucus using black tea waste was found to be 38.27 mg/ml. The best bioconversion efficiency was achieved at 40 °C with tannic acid concentration up to 200 g/L.