Systematic in silico analysis of clinically tested drugs for reducing amyloid-beta plaque accumulation in Alzheimer's disease.

INTRODUCTION: Despite strong evidence linking amyloid beta (Aß) to Alzheimer's disease, most clinical trials have shown no clinical efficacy for reasons that remain unclear. To understand why, we developed a quantitative systems pharmacology (QSP) model for seven therapeutics: aducanumab, crenezumab, solanezumab, bapineuzumab, elenbecestat, verubecestat, and semagacestat. METHODS: Ordinary differential equations were used to model the production, transport, and aggregation of Aß; pharmacology of the drugs; and their impact on plaque. RESULTS: The calibrated model predicts that endogenous plaque turnover is slow, with an estimated half-life of 2.75 years. This is likely why beta-secretase inhibitors have a smaller effect on plaque reduction. Of the mechanisms tested, the model predicts binding to plaque and inducing antibody-dependent cellular phagocytosis is the best approach for plaque reduction. DISCUSSION: A QSP model can provide novel insights to clinical results. Our model explains the results of clinical trials and provides guidance for future therapeutic development.

A quantitative systems pharmacology model of colonic motility with applications in drug development.

We developed a mathematical model of colon physiology driven by serotonin signaling in the enteric nervous system. No such models are currently available to assist drug discovery and development for GI motility disorders. Model parameterization was informed by published preclinical and clinical data. Our simulations provide clinically relevant readouts of bowel movement frequency and stool consistency. The model recapitulates healthy and slow transit constipation phenotypes, and the effect of a 5-HT4 receptor agonist in healthy volunteers. Using the calibrated model, we predicted the agonist dose to normalize defecation frequency in slow transit constipation while avoiding the onset of diarrhea. Model sensitivity analysis predicted that changes in HAPC frequency and liquid secretion have the greatest impact on colonic motility. However, exclusively increasing the liquid secretion can lead to diarrhea. In contrast, increasing HAPC frequency alone can enhance bowel frequency without leading to diarrhea. The quantitative systems pharmacology approach used here demonstrates how mechanistic modeling of disease pathophysiology expands our understanding of biology and supports judicious hypothesis generation for therapeutic intervention.

A Quantitative Systems Pharmacology Model Describing the Cellular Kinetic-Pharmacodynamic Relationship for a Live Biotherapeutic Product to Support Microbiome Drug Development.

Live biotherapeutic products (LBPs) are human microbiome therapies showing promise in the clinic for a range of diseases and conditions. Describing the kinetics and behavior of LBPs poses a unique modeling challenge because, unlike traditional therapies, LBPs can expand, contract, and colonize the host digestive tract. Here, we present a novel cellular kinetic-pharmacodynamic quantitative systems pharmacology model of an LBP. The model describes bacterial growth and competition, vancomycin effects, binding and unbinding to the epithelial surface, and production and clearance of butyrate as a therapeutic metabolite. The model is calibrated and validated to published data from healthy volunteers. Using the model, we simulate the impact of treatment dose, frequency, and duration as well as vancomycin pretreatment on butyrate production. This model enables model-informed drug development and can be used for future microbiome therapies to inform decision making around antibiotic pretreatment, dose selection, loading dose, and dosing duration.

A systems pharmacology model for gene therapy in sickle cell disease.

We developed a mathematical model for autologous stem cell therapy to cure sickle cell disease (SCD). Experimental therapies using this approach seek to engraft stem cells containing a curative gene. These stem cells are expected to produce a lifelong supply of red blood cells (RBCs) containing an anti-sickling hemoglobin. This complex, multistep treatment is expensive, and there is limited patient data available from early clinical trials. Our objective was to quantify the impact of treatment parameters, such as initial stem cell dose, efficiency of lentiviral transduction, and degree of bone marrow preconditioning on engraftment efficiency, peripheral RBC numbers, and anti-sickling hemoglobin levels over time. We used ordinary differential equations to model RBC production from progenitor cells in the bone marrow, and hemoglobin assembly from its constituent globin monomers. The model recapitulates observed RBC and hemoglobin levels in healthy and SCD phenotypes. Treatment simulations predict dynamics of stem cell engraftment and RBC containing the therapeutic gene product. Post-treatment dynamics show an early phase of reconstitution due to short lived stem cells, followed by a sustained RBC production from stable engraftment of long-term stem cells. This biphasic behavior was previously reported in the literature. Sensitivity analysis of the model quantified relationships between treatment parameters and efficacy. The initial dose of transduced stem cells, and the intensity of myeloablative bone marrow preconditioning are predicted to most positively impact long-term outcomes. The quantitative systems pharmacology approach used here demonstrates the value of model-assisted therapeutic design for gene therapies in SCD.

Virtual clinical trial simulations for a novel KRASG12C inhibitor (ASP2453) in non-small cell lung cancer.

KRAS is a small GTPase family protein that relays extracellular growth signals to cell nucleus. KRASG12C mutations lead to constitutive proliferation signaling and are prevalent across human cancers. ASP2453 is a novel, highly potent, and selective inhibitor of KRASG12C . Although preclinical data suggested impressive efficacy, it remains unclear whether ASP2453 will show more favorable clinical response compared to more advanced competitors, such as AMG 510. Here, we developed a quantitative systems pharmacology (QSP) model linking KRAS signaling to tumor growth in patients with non-small cell lung cancer. The model was parameterized using in vitro ERK1/2 phosphorylation and in vivo xenograft data for ASP2453. Publicly disclosed clinical data for AMG 510 were used to generate a virtual population, and tumor size changes in response to ASP2453 and AMG 510 were simulated. The QSP model predicted ASP2453 exhibits greater clinical response than AMG 510, supporting potential differentiation and critical thinking for clinical trials.

Quantitative modeling predicts competitive advantages of a next generation anti-NKG2A monoclonal antibody over monalizumab for the treatment of cancer.

A semimechanistic pharmacokinetic (PK)/receptor occupancy (RO) model was constructed to differentiate a next generation anti-NKG2A monoclonal antibody (KSQ mAb) from monalizumab, an immune checkpoint inhibitor in multiple clinical trials for the treatment of solid tumors. A three-compartment model incorporating drug PK, biodistribution, and NKG2A receptor interactions was parameterized using monalizumab PK, in vitro affinity measurements for both monalizumab and KSQ mAb, and receptor burden estimates from the literature. Following calibration against monalizumab PK data in patients with rheumatoid arthritis, the model successfully predicted the published PK and RO observed in gynecological tumors and in patients with squamous cell carcinoma of the head and neck. Simulations predicted that the KSQ mAb requires a 10-fold lower dose than monalizumab to achieve a similar RO over a 3-week period following q3w intravenous (i.v.) infusion dosing. A global sensitivity analysis of the model indicated that the drug-target binding affinity greatly affects the tumor RO and that an optimal affinity is needed to balance RO with enhanced drug clearance due to target mediated drug disposition. The model predicted that the KSQ mAb can be dosed over a less frequent regimen or at lower dose levels than the current monalizumab clinical dosing regimen of 10 mg/kg q2w. Either dosing strategy represents a competitive advantage over the current therapy. The results of this study demonstrate a key role for mechanistic modeling in identifying optimal drug parameters to inform and accelerate progression of mAb to clinical trials.

Epi-allelic Erk1 and Erk2 knockdown series for quantitative analysis of T cell Erk regulation and IL-2 production.

Erk activation is often used as a downstream pathway indicator of TCR signaling, generally in terms of both Erk1 and Erk2 isoforms measured together. In order to investigate potential distinctions between Erk1 and Erk2 regulation and effects downstream of TCR ligation, we generated a series of stable and independent Erk1 and Erk2 shRNA knockdown lines in the 1B6 T cell hybridoma. We observed no compensatory effect by opposite isoform upregulation, and found similar fractions of total phosphorylated Erk1/2 across this epi-allelic series in response to both anti-CD3 and peptide-MHC stimulation of TCR. Moreover, a previous prediction of an isoform-independent linear relationship between Erk activation and IL-2 production was confirmed. The effect of the shRNA-mediated knockdowns in reducing IL-2 production was observed to be stronger than that arising from pharmacological MEK inhibition at comparable degrees of ERK1/2 phosphorylation levels.

Cooperative cytotoxicity of proteasome inhibitors and tumor necrosis factor-related apoptosis-inducing ligand in chemoresistant Bcl-2-overexpressing cells.

PURPOSE: Bcl-2 overexpression is frequently detected in lymphoid malignancies, being associated with poor prognosis and reduced response to therapy. Here, we evaluated whether Bcl-2 overexpression affects the cytotoxic activity of proteasome inhibitors taken alone or in association with conventional anticancer drugs or tumor necrosis factor-related apoptosis-inducing ligand (TRAIL). EXPERIMENTAL DESIGN: Jurkat cells engineered to overexpress Bcl-2 were treated with proteasome inhibitors (MG132, epoxomicin, and bortezomib), anticancer drugs (etoposide and doxorubicin), TRAIL, or combinations of these compounds. Cell death and loss of mitochondrial transmembrane potential were detected by flow cytometry. Cytosolic relocalization of cytochrome c and SMAC/Diablo, caspase cleavage, and Bcl-2 and Mcl-1 levels were determined by immunoblotting. Nuclear factor-kappaB inhibition was done by retroviral transduction with a dominant-negative mutant of IkappaBalpha. RESULTS: Bcl-2 overexpression results in significant inhibition of apoptosis in response to proteasome inhibitors, antiblastics, and TRAIL. Addition of TRAIL to proteasome inhibitors results in a synergistic cytotoxic effect in Bcl-2-overexpressing cells, whereas this result is not reproduced by the combination of proteasome inhibitors with antiblastic drugs. Importantly, proteasome inhibitors plus TRAIL induce mitochondrial dysfunction irrespective of up-regulated Bcl-2. Bcl-2 cleavage to a fragment with putative proapoptotic activity and elimination of antiapoptotic Mcl-1 may both play a role in proteasome inhibitors-TRAIL cooperation. Conversely, nuclear factor-kappaB inhibition by proteasome inhibitors is per se insufficient to explain the observed synergy. CONCLUSIONS: Combined proteasome inhibitors and TRAIL overcome the apoptotic threshold raised by Bcl-2 and may prove useful in the treatment of chemoresistant malignancies with up-regulated Bcl-2.

Multiple subunits of the Caenorhabditis elegans anaphase-promoting complex are required for chromosome segregation during meiosis I.

Two genes, originally identified in genetic screens for Caenorhabditis elegans mutants that arrest in metaphase of meiosis I, prove to encode subunits of the anaphase-promoting complex or cyclosome (APC/C). RNA interference studies reveal that these and other APC/C subunits are essential for the segregation of chromosomal homologs during meiosis I. Further, chromosome segregation during meiosis I requires APC/C functions in addition to the release of sister chromatid cohesion.

Seribantumab, an Anti-ERBB3 Antibody, Delays the Onset of Resistance and Restores Sensitivity to Letrozole in an Estrogen Receptor-Positive Breast Cancer Model.

Heregulin-driven ERBB3 signaling has been implicated as a mechanism of resistance to cytotoxic and antiendocrine therapies in preclinical breast cancer models. In this study, we evaluated the effects of seribantumab (MM-121), a heregulin-blocking anti-ERBB3 monoclonal antibody, alone and in combination with the aromatase inhibitor letrozole, on cell signaling and tumor growth in a preclinical model of postmenopausal estrogen receptor-positive (ER(+)) breast cancer. In vitro, heregulin treatment induced estrogen receptor phosphorylation in MCF-7Ca cells, and long-term letrozole-treated (LTLT-Ca) cells had increased expression and activation levels of EGFR, HER2, and ERBB3. Treatment with seribantumab, but not letrozole, inhibited basal and heregulin-mediated ERBB receptor phosphorylation and downstream effector activation in letrozole-sensitive (MCF-7Ca) and -refractory (LTLT-Ca) cells. Notably, in MCF-7Ca-derived xenograft tumors, cotreatment with seribantumab and letrozole had increased antitumor activity compared with letrozole alone, which was accompanied by downregulated PI3K/MTOR signaling both prior to and after the development of resistance to letrozole. Moreover, the addition of an MTOR inhibitor to this treatment regimen did not improve antitumor activity and was not well tolerated. Our results demonstrate that heregulin-driven ERBB3 signaling mediates resistance to letrozole in a preclinical model of ER(+) breast cancer, suggesting that heregulin-expressing ER(+) breast cancer patients may benefit from the addition of seribantumab to antiendocrine therapy.

Quantitative network signal combinations downstream of TCR activation can predict IL-2 production response.

Proximal signaling events activated by TCR-peptide/MHC (TCR-pMHC) binding have been the focus of intense ongoing study, but understanding how the consequent downstream signaling networks integrate to govern ultimate avidity-appropriate TCR-pMHC T cell responses remains a crucial next challenge. We hypothesized that a quantitative combination of key downstream network signals across multiple pathways must encode the information generated by TCR activation, providing the basis for a quantitative model capable of interpreting and predicting T cell functional responses. To this end, we measured 11 protein nodes across six downstream pathways, along five time points from 10 min to 4 h, in a 1B6 T cell hybridoma stimulated by a set of three myelin proteolipid protein 139-151 altered peptide ligands. A multivariate regression model generated from this data compendium successfully comprehends the various IL-2 production responses and moreover successfully predicts a priori the response to an additional peptide treatment, demonstrating that TCR binding information is quantitatively encoded in the downstream network. Individual node and/or time point measurements less effectively accounted for the IL-2 responses, indicating that signals must be integrated dynamically across multiple pathways to adequately represent the encoded TCR signaling information. Of further importance, the model also successfully predicted a priori direct experimental tests of the effects of individual and combined inhibitors of the MEK/ERK and PI3K/Akt pathways on this T cell response. Together, our findings show how multipathway network signals downstream of TCR activation quantitatively integrate to translate pMHC stimuli into functional cell responses.