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What Is This Summary About?: In this article, we summarize results from a clinical study called LOTIS-2, in which researchers looked at patients with a type of blood cancer called diffuse large B-cell lymphoma, or DLBCL for short. Patients received the drug loncastuximab tesirine, or Lonca for short, which targets a marker on the surface of tumor cells called CD19.Patient information from the LOTIS-2 study, other studies of Lonca, and information from scientific publications was used to develop a quantitative systems pharmacology (QSP) model, which can predict how Lonca works in the body. The goal was to use the QSP model to see if CD19 levels can predict tumor size changes after Lonca treatment and if Lonca can still work to treat DLBCL when CD19 levels are very low. The prior LOTIS-1 and LOTIS-2 trials demonstrated an acceptable safety profile for Lonca, and therefore the current study did not evaluate safety data., What Were the Results?: Researchers used immunohistochemistry, a common technique to evaluate CD19 expression. They found that there was no association between patients who responded to Lonca treatment and levels of CD19 on their tumor cells. Some patients with low or even undetectable levels of CD19 on their tumor cells had observable decreases in tumor size after Lonca treatment., What Do the Results of the Study Mean?: While Lonca uses the CD19 target to find and destroy cancer cells, Lonca does not require a large amount of CD19 to kill tumor cells. These results mean that Lonca may be an effective treatment for patients with DLBCL, even if CD19 expression in tumors is undetectable by immunohistochemistry.

Population pharmacokinetic (PK) modeling serves as the cornerstone for understanding drug behavior within a specific population. It integrates subject covariates to elucidate the variability in PK parameters, thus enhancing predictive accuracy. However, covariate modeling within this framework can be intricate and time-consuming due to the often obscure structural relationship between covariates and PK parameters. Previous attempts, such as deep compartment modeling (DCM), aimed to streamline this process using machine learning techniques. Nonetheless, DCM fell short in assessing residual errors and interindividual variability (IIV), potentially leading to model misspecification and overfitting. Furthermore, DCM lacked the ability to quantify model uncertainty. To address these limitations, we introduce hierarchical deep compartment modeling (HDCM) as an advancement of DCM. HDCM harnesses machine learning to discern the interplay between covariates and PK parameters while simultaneously evaluating diverse levels of random effects and quantifying uncertainty through Bayesian inference. This tutorial provides a comprehensive application of the HDCM workflow using open-source Julia tools., (© 2024 The Author(s). Clinical and Translational Science published by Wiley Periodicals LLC on behalf of American Society for Clinical Pharmacology and Therapeutics.)

CD19-targeting treatments have shown promise in relapsed/refractory (R/R) diffuse large B-cell lymphoma (DLBCL). Loncastuximab tesirine (loncastuximab tesirine-lpyl [Lonca]) is a CD19-targeting antibody-drug conjugate indicated for R/R DLBCL after at least two systemic treatments. CD19 expression was evaluated in patients receiving Lonca in the LOTIS-2 clinical trial with available tissue samples obtained after last systemic therapy/before Lonca treatment. Lonca cytotoxicity was evaluated in a panel of six lymphoma cell lines with various CD19 expression levels. Quantitative systems pharmacology (QSP) modelling was used to predict Lonca responses. Lonca responses were seen in patients across all CD19 expression levels, including patients with low/no detectable CD19 expression and H-scores at baseline. Similarly, Lonca induced cytotoxicity in cell lines with different levels of CD19 expression, including one with very low expression. QSP modelling predicted that CD19 expression by immunohistochemistry alone does not predict Lonca response, whereas inclusion of CD19 surface density improved response prediction. Virtual patients responded to Lonca with estimated CD19 as low as 1000 molecules/cell of CD19, normally below the immunohistochemistry detection level. We found Lonca is an effective treatment for R/R DLBCL regardless of CD19 expression by immunohistochemistry. These results provide the basis for future studies addressing CD19-targeted agent sequencing., Competing Interests: PFC was on the advisory board for ADC Therapeutics SA, Amgen, BeiGene, Bristol Myers Squibb (BMS), Genentech, Kite, MEI Pharmaceuticals and Novartis and received research funding from AbbVie, ADC Therapeutics SA, and Genentech. MH has been a consultant with AbbVie, ADC Therapeutics SA, BMS, Caribou, CRISPR, Gamida Cell, Genmab, Incyte Corporation, Kadmon, Kite, Legend Biotech, MorphoSys, Novartis, Omeros and Seagen; received research funding from ADC Therapeutics SA and Spectrum Pharmaceuticals; has speakers bureau memberships for ADC Therapeutics SA, AstraZeneca, BeiGene, Kite, and Sanofi Genzyme and participated on data monitoring committees for Genentech and Myeloid Therapeutics. CC‐S has been a consultant with Sanofi; has been on the board of directors, speakers bureau or advisory committee for ADC Therapeutics SA, BMS, Celgene, Karyopharm, Roche and Sanofi; has received research funding from ADC Therapeutics SA, Roche and Sanofi and has received honoraria from ADC Therapeutics SA, AstraZeneca, BMS, Incyte, Janssen Oncology, and Takeda. MN is currently employed at Boehringer Ingelheim Pharmaceuticals, Inc. EJ was an employee of Metrum Research Group at the time of the study. KU and TK are currently employed at Metrum Research Group. FZ and YW were employees of ADC Therapeutics SA, a publicaly traded company, at the time of the study and may hold stock. DC, SP and JB are currently employed and current stockholders at ADC Therapeutics SA, a publicly traded company. KH is currently employed and a current stockholder at ADC Therapeutics (UK) Ltd, a publicly traded company. ETHICS STATEMENTThe authors have confirmed an ethical approval statement is not needed for this submission., (© 2023 The Authors. eJHaem published by British Society for Haematology and John Wiley & Sons Ltd.)

Millions of people worldwide develop foodborne illnesses caused by Salmonella enterica (S. enterica) every year. The pathogenesis of S. enterica depends on flagella, which are appendages that the bacteria use to move through the environment. Interestingly, populations of genetically identical bacteria exhibit heterogeneity in the number of flagella. To understand this heterogeneity and the regulation of flagella quantity, we propose a mathematical model that connects the flagellar gene regulatory network to flagellar construction. A regulatory network involving more than 60 genes controls flagellar assembly. The most important member of the network is the master operon, flhDC, which encodes the FlhD4C2 protein. FlhD4C2 controls the construction of flagella by initiating the production of hook basal bodies (HBBs), protein structures that anchor the flagella to the bacterium. By connecting a model of FlhD4C2 regulation to a model of HBB construction, we investigate the roles of various feedback mechanisms. Analysis of our model suggests that a combination of regulatory mechanisms at the protein and transcriptional levels induce bistable FlhD4C2 levels and heterogeneous numbers of flagella. Also, the balance of regulatory mechanisms that become active following HBB construction is sufficient to provide a counting mechanism for controlling the total number of flagella produced., Competing Interests: The authors have declared that no competing interests exist.

Tissue:plasma partition coefficients are key parameters in physiologically based pharmacokinetic (PBPK) models, yet the coefficients are challenging to measure in vivo. Several mechanistic-based equations have been developed to predict partition coefficients using tissue composition information and the compound's physicochemical properties, but it is not clear which, if any, of the methods is most appropriate under given circumstances. Complicating the evaluation, each prediction method was developed, and is typically employed, using a different set of tissue composition information, thereby making a controlled comparison impossible. This study proposed a standardized tissue composition for humans that can be used as a common input for each of the five frequently used prediction methods. These methods were implemented in R and were used to predict partition coefficients for 11 drugs, classified as strong bases, weak bases, acids, neutrals, and zwitterions. PBPK models developed in R ( mrgsolve ) for each drug and each set of partition coefficient predictions were compared with respective observed plasma concentration data. Percent root mean square error and half-life percent error were used to evaluate the accuracy of the PBPK model predictions using each partition coefficient method as summarized by strong bases, weak bases, acids, neutrals, and zwitterions characterization. The analysis indicated that no partition coefficient method consistently yielded the most accurate PBPK model predictions. As such, PBPK model predictions using all partition coefficient methods should be considered during drug development. SIGNIFICANCE STATEMENT: Several mechanistic-based methods exist to predict tissue:plasma partition coefficients critical to PBPK modeling. Controlled comparisons are confounded by the use of different tissue composition values for each method; a standardized tissue composition was proposed. Resulting assessments indicated that no method was consistently superior; therefore, sensitivity of PBPK predictions to each method may be warranted prior to model optimization., (Copyright © 2020 The Author(s).)

DNA methylation is an essential epigenetic mechanism used by cells to regulate gene expression. Interestingly, DNA replication, a function necessary for cell division, disrupts the methylation pattern. Since perturbed methylation patterns are associated with aberrant gene expression and many diseases, including cancer, restoration of the correct pattern following DNA replication is crucial. However, the exact mechanisms of this restoration remain under investigation. DNA methyltransferases (Dnmts) perform methylation by adding a methyl group to cytosines at CpG sites in the DNA. These CpG sites are found in regions of high density, termed CpG islands (CGIs), and regions of low density in the genome. Nearly, every CpG site in a CGI has the same state, either methylated or unmethylated, and almost all CpG sites in regions of low CpG density are methylated. We propose a stochastic model for the dynamics of the post-replicative restoration of methylation patterns. The model considers the recruitment of Dnmts and demethylating enzymes to regions of hyper- and hypomethylation, respectively. The model also includes the interaction between Dnmt1 and PCNA, an enzyme that localizes Dnmt1 to the replication complex. Using our model, we predict that the methylation of regions of DNA can be bistable. Further, we predict that recruitment mechanisms maintain methylation in CGIs, whereas the Dnmt1-PCNA interaction maintains methylation in low-density regions.