Retrospective Analysis Using Pharmacokinetic/Pharmacodynamic Modeling and Simulation Offers Improvements in Efficiency of the Design of Volunteer Infection Studies for Antimalarial Drug Development

Volunteer infection studies using the induced blood stage malaria (IBSM) model have been shown to facilitate antimalarial drug development. Such studies have traditionally been undertaken in single‐dose cohorts, as many as necessary to obtain the dose‐response relationship. To enhance ethical and logistic aspects of such studies, and to reduce the number of cohorts needed to establish the dose‐response relationship, we undertook a retrospective in silico analysis of previously accrued data to improve study design. A pharmacokinetic (PK)/pharmacodynamic (PD) model was developed from initial fictive‐cohort data for OZ439 (mixing the data of the three single‐dose cohorts as: n = 2 on 100 mg, 2 on 200 mg, and 4 on 500 mg). A three‐compartment model described OZ439 PKs. Net growth of parasites was modeled using a Gompertz function and drug‐induced parasite death using a Hill function. Parameter estimates for the PK and PD models were comparable for the multidose single‐cohort vs. the pooled analysis of all cohorts. Simulations based on the multidose single‐cohort design described the complete data from the original IBSM study. The novel design allows for the ascertainment of the PK/PD relationship early in the study, providing a basis for rational dose selection for subsequent cohorts and studies.

Study Highlights

• WHAT IS THE CURRENT KNOWLEDGE ON THE TOPIC?

☑ Volunteer infection studies are routinely used in antimalarial drug development to generate early pharmacokinetic/pharmacodynamic data for compounds.

• WHAT QUESTION DID THIS STUDY ADDRESS?

☑ Can in silico analyses be used to suggest improvements to volunteer infection study designs?

• WHAT DOES THIS STUDY ADD TO OUR KNOWLEDGE?

☑ Multiple dose adaptive trial designs can potentially reduce the number of cohorts needed to establish the dose‐response relationship in volunteer infection studies.

• HOW MIGHT THIS CHANGE CLINICAL PHARMACOLOGY OR TRANSLATIONAL SCIENCE?

☑ Real time data analyses can be used to recommend doses for adaptive volunteer infection studies.

Volunteer infection studies using the induced blood stage malaria (IBSM) model have been recognized as a valuable system for defining the key pharmacokinetic (PK) and pharmacodynamic (PD) relationships for dose selection in antimalarial drug development. ¹ , ² , ³ , ⁴ , ⁵ , ⁶ , ⁷ In such studies, healthy volunteers are inoculated intravenously with a given quantity (with small variability) of Plasmodium‐infected red cells. Parasitemia is then followed by quantitative polymerase chain reaction until a prespecified treatment threshold is reached when the test drug is administered. Parasite and drug concentrations are then measured. These studies are conducted prior to phase II dose‐response (D‐R) trials and can be included in an integrated first‐in‐human study protocol, or after completion of the first‐in‐human PK and safety study. IBSM studies have been typically designed as flexible multiple cohort studies where each volunteer of one cohort receives a single dose of the same amount of drug (“single dose per cohort”). ² , ³ , ⁴ , ⁵ After each cohort, a decision is made to stop or to add a cohort to test a lower or higher dose based on the response observed in the previous cohorts.For the multiple single‐dose‐per‐cohort design, the starting dose is typically selected based on safety and PK information from a phase I single ascending dose (SAD) study and, more recently, on preclinical data from a severe combined immunodeficient mouse model, with the dose selected on the basis of being best able to inform the D‐R relationship, rather than aiming for cure. This approach, where a single dose is tested in all subjects of the initial cohort, risks missing the dose likely to be most informative for defining the PK/PD relationship.An alternative approach is to spread a range of doses across a smaller number of subjects within the initial cohort and use PK/PD models developed based on data from this cohort to support dose selections of subsequent cohorts and studies. Using data from a previous study, ² we undertook an in silico investigation of such an adaptive study design, aiming to reduce the number of subjects exposed to inefficacious doses, and to establish a D‐R relationship. This multiple‐dose‐groups‐per‐cohort design, referred to as the “2‐2‐4” design, is contrasted with the already implemented study design depicted in Figure  1 .Open in a separate windowFigure 1Comparison of standard and adaptive designs of IBSM studies. A/B/C, dose levels to be selected during the progress of the study based on pharmacokinetic/pharmacodynamic results of the initial cohort; CHMI, controlled human malaria infection; D‐R, dose‐response; IBSM, induced blood stage malaria infection; n, number of subjects at each dose.The objectives of this retrospective analysis were to: (i) compare PK/PD parameter estimates from the initial cohort of the 2‐2‐4 study design with the prior results from the data of the full study and (ii) propose a preliminary workflow to establish D‐R early in an IBSM study, and use modeling and simulation (M&S) to support dose selections for subsequent cohorts and later phase clinical trials.     

Kinetic Investigations of Quaternization Reactions of Poly[2‐(dimethylamino)ethyl methacrylate] with Diverse Alkyl Halides

Kinetic investigations of the quaternization reactions of poly[2‐(dimethylamino)ethyl methacrylate] (PDMAEMA) with alkyl halides (1‐iodobutane, 1‐iodoheptane, and 1‐iododecane) are carried out at different temperatures. For this purpose, a PDMAEMA (M_n = 17.8 kDa, Ð = 1.35) synthesized via reversible addition fragmentation chain transfer polymerization is utilized. The progress of the quaternization reactions is followed by proton nuclear magnetic resonance. As expected, the rate of quaternization is higher with increasing temperature. The experimental data are used to determine the following kinetic parameters: order of the reaction, Arrhenius' pre‐exponential factor, and activation energy. To the best of knowledge, this is the first contribution that provides detailed kinetic data of the quaternization reactions on PDMAEMA.     

Peripheral whole blood FOXP3 TSDR methylation: a potential marker in severity assessment of autoimmune diseases and chronic infections

Immune dysregulation is a cardinal feature of autoimmune diseases and chronic microbial infections. In particular, regulatory T cells are downregulated in autoimmune diseases while upregulated in chronic microbial infections. FOXP3 is the master regulator of Treg development. Treg-specific demethylated region (TSDR) is a highly conserved locus on the FOXP3 gene that is fully demethylated in natural Tregs but methylated in effector T cells. In our study, we used high resolution melt-polymerase chain reaction (HRM-PCR) to determine the FOXP3 TSDR methylation status in autoimmune diseases and chronic microbial infections. We found that FOXP3 TSDR to have the highest mean melting temperature (highly methylated) in active SLE patients compared to all the other groups (p?<?0.001). The psoriasis group also had a significantly high mean melting temperature (78.62?±?0.20) when compared with the inactive SLE group (78.49?±?0.29, p?<?0.05) and control group (78.44?±?0.25, p?<?0.01). There was no significant difference in melting temperature between inactive SLE and healthy controls. Disease activity in SLE was directly associated with methylation of the FOXP3 TSDR. On the other hand, patients with chronic microbial infections had significantly lower FOXP3 TSDR mean melting temperature (demethylated) when compared with healthy controls (78.28?±?0.21 vs 78.44?±?0.25, p?<?0.05). Our results suggest that the use of HRM-PCR to detect FOXP3 TSDR methylation status is a reliable and easy method to predict natural regulatory T cell levels in peripheral blood in different disease conditions. Determining FOXP3 TSDR methylation status can be a useful tool in diagnosis, and monitoring the severity of autoimmune diseases and chronic microbial infections.