Hierarchical deep compartment modeling: A workflow to leverage machine learning and Bayesian inference for hierarchical pharmacometric modeling.

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.

Making drugs from T cells: The quantitative pharmacology of engineered T cell therapeutics.

Engineered T cells have emerged as highly effective treatments for hematological cancers. Hundreds of clinical programs are underway in efforts to expand the efficacy, safety, and applications of this immuno-therapeutic modality. A primary challenge in developing these "living drugs" is the complexity of their pharmacology, as the drug product proliferates, differentiates, traffics between tissues, and evolves through interactions with patient immune systems.

Deconvolution of clinical variance in CAR-T cell pharmacology and response.

Chimeric antigen receptor T cell (CAR-T) expansion and persistence vary widely among patients and predict both efficacy and toxicity. However, the mechanisms underlying clinical outcomes and patient variability are poorly defined. In this study, we developed a mathematical description of T cell responses wherein transitions among memory, effector and exhausted T cell states are coordinately regulated by tumor antigen engagement.

Virtual clinical trial simulations for a novel KRAS inhibitor (ASP2453) in non-small cell lung cancer.

KRAS is a small GTPase family protein that relays extracellular growth signals to cell nucleus. KRAS mutations lead to constitutive proliferation signaling and are prevalent across human cancers. ASP2453 is a novel, highly potent, and selective inhibitor of KRAS .

Reproducibility of Quantitative Systems Pharmacology Models: Current Challenges and Future Opportunities.

The provision of model code is required for publication in CPT: Pharmacometrics & Systems Pharmacology, enabling quantitative systems pharmacology (QSP) model availability. A searchable repository of published QSP models would enhance model accessibility. We assess the feasibility of establishing such a resource based on 18 QSP models published in this journal.

A transcriptional MAPK Pathway Activity Score (MPAS) is a clinically relevant biomarker in multiple cancer types.

- and -mutant tumors are often dependent on MAPK signaling for proliferation and survival and thus sensitive to MAPK pathway inhibitors. However, clinical studies have shown that MEK inhibitors are not uniformly effective in these cancers indicating that mutational status of these oncogenes does not accurately capture MAPK pathway activity. A number of transcripts are regulated by this pathway and are recurrently identified in genome-based MAPK transcriptional signatures.

Correction: Combined MEK and ERK inhibition overcomes therapy-mediated pathway reactivation in RAS mutant tumors.

[This corrects the article DOI: 10.1371/journal.pone.

Combined MEK and ERK inhibition overcomes therapy-mediated pathway reactivation in RAS mutant tumors.

Mitogen-activated protein kinase (MAPK) pathway dysregulation is implicated in >30% of all cancers, rationalizing the development of RAF, MEK and ERK inhibitors. While BRAF and MEK inhibitors improve BRAF mutant melanoma patient outcomes, these inhibitors had limited success in other MAPK dysregulated tumors, with insufficient pathway suppression and likely pathway reactivation. In this study we show that inhibition of either MEK or ERK alone only transiently inhibits the MAPK pathway due to feedback reactivation.

Erratum: Clinical responses to ERK inhibition in -mutant colorectal cancer predicted using a computational model.

[This corrects the article DOI: 10.1038/s41540-017-0016-1.].

Clinical responses to ERK inhibition in -mutant colorectal cancer predicted using a computational model.

Approximately 10% of colorectal cancers harbor mutations, which constitutively activate the MAPK signaling pathway. We sought to determine whether ERK inhibitor (GDC-0994)-containing regimens may be of clinical benefit to these patients based on data from in vitro (cell line) and in vivo (cell- and patient-derived xenograft) studies of cetuximab (EGFR), vemurafenib (BRAF), cobimetinib (MEK), and GDC-0994 (ERK) combinations. Preclinical data was used to develop a mechanism-based computational model linking cell surface receptor (EGFR) activation, the MAPK signaling pathway, and tumor growth.

HER2+ Cancer Cell Dependence on PI3K vs. MAPK Signaling Axes Is Determined by Expression of EGFR, ERBB3 and CDKN1B.

Understanding the molecular pathways by which oncogenes drive cancerous cell growth, and how dependence on such pathways varies between tumors could be highly valuable for the design of anti-cancer treatment strategies. In this work we study how dependence upon the canonical PI3K and MAPK cascades varies across HER2+ cancers, and define biomarkers predictive of pathway dependencies. A panel of 18 HER2+ (ERBB2-amplified) cell lines representing a variety of indications was used to characterize the functional and molecular diversity within this oncogene-defined cancer.

Computational modeling of ERBB2-amplified breast cancer identifies combined ErbB2/3 blockade as superior to the combination of MEK and AKT inhibitors.

Crosstalk and compensatory circuits within cancer signaling networks limit the activity of most targeted therapies. For example, altered signaling in the networks activated by the ErbB family of receptors, particularly in ERBB2-amplified cancers, contributes to drug resistance. We developed a multiscale systems model of signaling networks in ERBB2-amplified breast cancer to quantitatively investigate relationships between biomarkers (markers of network activity) and combination drug efficacy.

Creating and analyzing pathway and protein interaction compendia for modelling signal transduction networks.

Background: Understanding the information-processing capabilities of signal transduction networks, how those networks are disrupted in disease, and rationally designing therapies to manipulate diseased states require systematic and accurate reconstruction of network topology. Data on networks central to human physiology, such as the inflammatory signalling networks analyzed here, are found in a multiplicity of on-line resources of pathway and interactome databases (Cancer CellMap, GeneGo, KEGG, NCI-Pathway Interactome Database (NCI-PID), PANTHER, Reactome, I2D, and STRING). We sought to determine whether these databases contain overlapping information and whether they can be used to construct high reliability prior knowledge networks for subsequent modeling of experimental data.

Rapid expansion of human hematopoietic stem cells by automated control of inhibitory feedback signaling.

Clinical hematopoietic transplantation outcomes are strongly correlated with the numbers of cells infused. Anticipated novel therapeutic implementations of hematopoietic stem cells (HSCs) and their derivatives further increase interest in strategies to expand HSCs ex vivo. A fundamental limitation in all HSC-driven culture systems is the rapid generation of differentiating cells and their secreted inhibitory feedback signals.

The AC133+CD38-, but not the rhodamine-low, phenotype tracks LTC-IC and SRC function in human cord blood ex vivo expansion cultures.

Phenotypic markers associated with human hematopoietic stem cells (HSCs) were developed and validated using uncultured cells. Because phenotype and function can be dissociated during culture, better markers to prospectively track and isolate HSCs in ex vivo cultures could be instrumental in advancing HSC-based therapies. Using an expansion system previously shown to increase hematopoietic progenitors and SCID-repopulating cells (SRCs), we demonstrated that the rhodamine-low phenotype was lost, whereas AC133 expression was retained throughout culture.

Cell-cell interaction networks regulate blood stem and progenitor cell fate.

Communication networks between cells and tissues are necessary for homeostasis in multicellular organisms. Intercellular (between cell) communication networks are particularly relevant in stem cell biology, as stem cell fate decisions (self-renewal, proliferation, lineage specification) are tightly regulated based on physiological demand. We have developed a novel mathematical model of blood stem cell development incorporating cell-level kinetic parameters as functions of secreted molecule-mediated intercellular networks.

The systematic production of cells for cell therapies.

Stem cells have emerged as the starting material of choice for bioprocesses to produce cells and tissues to treat degenerative, genetic, and immunological disease. Translating the biological properties and potential of stem cells into therapies will require overcoming significant cell-manufacturing and regulatory challenges. Bioprocess engineering fundamentals, including bioreactor design and process control, need to be combined with cellular systems biology principles to guide the development of next-generation technologies capable of producing cell-based products in a safe, robust, and cost-effective manner.

Clinically relevant expansion of hematopoietic stem cells with conserved function in a single-use, closed-system bioprocess.

The clinical potential of umbilical cord blood-derived stem and progenitor cells has been demonstrated in various animal and human transplantation studies. However, the need for increased numbers of appropriate umbilical cord blood-derived cells continues to limit the development and success of these therapies. Ex vivo expansion has been widely studied as a method to overcome this limitation.

Understanding cellular networks to improve hematopoietic stem cell expansion cultures.

Efforts to develop culture technologies capable of eliciting robust human blood stem cell growth have met with limited success. Considering that adult stem cell cultures are complex systems, comprising multiple cell types with dynamically changing intracellular signalling environments and cellular compositions, this is not surprising. Typically treated as single-input single-output systems, adult stem cell cultures are better described as complex, non-linear, multiple-input multiple-output systems wherein the proliferation of subpopulations of cells leads to the formation of intercellular endogenously secreted protein interaction networks.

Dynamic changes in cellular and microenvironmental composition can be controlled to elicit in vitro human hematopoietic stem cell expansion.

Objective: The absence of effective strategies for the ex vivo expansion of human hematopoietic stem cells (HSCs) limits the development of many cell-based therapies. Prior attempts to stimulate HSC expansion have focused on media supplementation using cytokines and growth factors. In these cultures, cellular and microenvironmental compositions change with time.