Network modeling predicts personalized gene expression and drug responses in valve myofibroblasts cultured with patient sera.

Aortic valve stenosis (AVS) patients experience pathogenic valve leaflet stiffening due to excessive extracellular matrix (ECM) remodeling. Numerous microenvironmental cues influence pathogenic expression of ECM remodeling genes in tissue-resident valvular myofibroblasts, and the regulation of complex myofibroblast signaling networks depends on patient-specific extracellular factors. Here, we combined a manually curated myofibroblast signaling network with a data-driven transcription factor network to predict patient-specific myofibroblast gene expression signatures and drug responses. Using transcriptomic data from myofibroblasts cultured with AVS patient sera, we produced a large-scale, logic-gated differential equation model in which 11 biochemical and biomechanical signals were transduced via a network of 334 signaling and transcription reactions to accurately predict the expression of 27 fibrosis-related genes. Correlations were found between personalized model-predicted gene expression and AVS patient echocardiography data, suggesting links between fibrosis-related signaling and patient-specific AVS severity. Further, global network perturbation analyses revealed signaling molecules with the most influence over network-wide activity, including endothelin 1 (ET1), interleukin 6 (IL6), and transforming growth factor ß (TGFß), along with downstream mediators c-Jun N-terminal kinase (JNK), signal transducer and activator of transcription (STAT), and reactive oxygen species (ROS). Lastly, we performed virtual drug screening to identify patient-specific drug responses, which were experimentally validated via fibrotic gene expression measurements in valvular interstitial cells cultured with AVS patient sera and treated with or without bosentan-a clinically approved ET1 receptor inhibitor. In sum, our work advances the ability of computational approaches to provide a mechanistic basis for clinical decisions including patient stratification and personalized drug screening.

Ensemble machine learning model identifies patients with HFpEF from matrix-related plasma biomarkers.

Arterial hypertension can lead to structural changes within the heart including left ventricular hypertrophy (LVH) and eventually heart failure with preserved ejection fraction (HFpEF). The initial diagnosis of HFpEF is costly and generally based on later stage remodeling; thus, improved predictive diagnostic tools offer potential clinical benefit. Recent work has shown predictive value of multibiomarker plasma panels for the classification of patients with LVH and HFpEF. We hypothesized that machine learning algorithms could substantially improve the predictive value of circulating plasma biomarkers by leveraging more sophisticated statistical approaches. In this work, we developed an ensemble classification algorithm for the diagnosis of HFpEF within a population of 480 individuals including patients with HFpEF, patients with LVH, and referent control patients. Algorithms showed strong diagnostic performance with receiver-operating-characteristic curve (ROC) areas of 0.92 for identifying patients with LVH and 0.90 for identifying patients with HFpEF using demographic information, plasma biomarkers related to extracellular matrix remodeling, and echocardiogram data. More impressively, the ensemble algorithm produced an ROC area of 0.88 for HFpEF diagnosis using only demographic and plasma panel data. Our findings demonstrate that machine learning-based classification algorithms show promise as a noninvasive diagnostic tool for HFpEF, while also suggesting priority biomarkers for future mechanistic studies to elucidate more specific regulatory roles.NEW & NOTEWORTHY Machine learning algorithms correctly classified patients with heart failure with preserved ejection fraction with over 90% area under receiver-operating-characteristic curves. Classifications using multidomain features (demographics and circulating biomarkers and echo-based ventricle metrics) proved more accurate than previous studies using single-domain features alone. Excitingly, HFpEF diagnoses were generally accurate even without echo-based measurements, demonstrating that such algorithms could provide an early screening tool using blood-based measurements before sophisticated imaging.

The connexin 43 carboxyl terminal mimetic peptide αCT1 prompts differentiation of a collagen scar matrix in humans resembling unwounded skin.

Phase II clinical trials have reported that acute treatment of surgical skin wounds with the therapeutic peptide alpha Connexin Carboxy-Terminus 1 (αCT1) improves cutaneous scar appearance by 47% 9-month postsurgery. While Cx43 and ZO-1 have been identified as molecular targets of αCT1, the mode-of-action of the peptide in scar mitigation at cellular and tissue levels remains to be further characterized. Scar histoarchitecture in αCT1 and vehicle-control treated skin wounds within the same patient were compared using biopsies from a Phase I clinical trial at 29-day postwounding. The sole effect on scar structure of a range of epidermal and dermal variables examined was that αCT1-treated scars had less alignment of collagen fibers relative to control wounds-a characteristic that resembles unwounded skin. The with-in subject effect of αCT1 on scar collagen order observed in Phase I testing in humans was recapitulated in Sprague-Dawley rats and the IAF hairless guinea pig. Transient increase in histologic collagen density in response to αCT1 was also observed in both animal models. Mouse NIH 3T3 fibroblasts and primary human dermal fibroblasts treated with αCT1 in vitro showed more rapid closure in scratch wound assays, with individual cells showing decreased directionality in movement. An agent-based computational model parameterized with fibroblast motility data predicted collagen alignments in simulated scars consistent with that observed experimentally in human and the animal models. In conclusion, αCT1 prompts decreased directionality of fibroblast movement and the generation of a 3D collagen matrix postwounding that is similar to unwounded skin-changes that correlate with long-term improvement in scar appearance.

Interpretable machine learning predicts cardiac resynchronization therapy responses from personalized biochemical and biomechanical features.

BACKGROUND: Cardiac Resynchronization Therapy (CRT) is a widely used, device-based therapy for patients with left ventricle (LV) failure. Unfortunately, many patients do not benefit from CRT, so there is potential value in identifying this group of non-responders before CRT implementation. Past studies suggest that predicting CRT response will require diverse variables, including demographic, biomarker, and LV function data. Accordingly, the objective of this study was to integrate diverse variable types into a machine learning algorithm for predicting individual patient responses to CRT. METHODS: We built an ensemble classification algorithm using previously acquired data from the SMART-AV CRT clinical trial (n = 794 patients). We used five-fold stratified cross-validation on 80% of the patients (n = 635) to train the model with variables collected at 0 months (before initiating CRT), and the remaining 20% of the patients (n = 159) were used as a hold-out test set for model validation. To improve model interpretability, we quantified feature importance values using SHapley Additive exPlanations (SHAP) analysis and used Local Interpretable Model-agnostic Explanations (LIME) to explain patient-specific predictions. RESULTS: Our classification algorithm incorporated 26 patient demographic and medical history variables, 12 biomarker variables, and 18 LV functional variables, which yielded correct prediction of CRT response in 71% of patients. Additional patient stratification to identify the subgroups with the highest or lowest likelihood of response showed 96% accuracy with 22 correct predictions out of 23 patients in the highest and lowest responder groups. CONCLUSION: Computationally integrating general patient characteristics, comorbidities, therapy history, circulating biomarkers, and LV function data available before CRT intervention can improve the prediction of individual patient responses.

Effects of Collagen Heterogeneity on Myocardial Infarct Mechanics in a Multiscale Fiber Network Model.

The scar that forms after a myocardial infarction is often characterized by a highly disordered architecture but generally exhibits some degree of collagen fiber orientation, with a resulting mechanical anisotropy. When viewed in finer detail, however, the heterogeneity of the sample is clear, with different subregions exhibiting different fiber orientations. In this work, we used a multiscale finite element model to explore the consequences of the heterogeneity in terms of mechanical behavior. To do so, we used previously obtained fiber alignment maps of rat myocardial scar slices (n = 15) to generate scar-specific finite element meshes that were populated with fiber models based on the local alignment state. These models were then compared to isotropic models with the same sample shape and fiber density, and to homogeneous models with the same sample shape, fiber density, and average fiber alignment as the scar-specific models. All simulations involved equibiaxial extension of the sample with free motion in the third dimension. We found that heterogeneity led to a lower degree of mechanical anisotropy and a higher level of local stress concentration than the corresponding homogeneous model, and also that fibers failed in the heterogeneous model at much lower macroscopic strains than in the isotropic and homogeneous models. Taken together, these results suggest that scar heterogeneity may impair myocardial mechanical function both in terms of anisotropy and strength, and that individual variations in scar heterogeneity could be an important consideration for understanding scar remodeling and designing therapeutic interventions for patients after myocardial infarction.

Development of a Global Design Education Experience in Bioengineering Through International Partnerships.

The field of engineering is increasingly appreciating the value of diversity for innovative design solutions. Successful engineering depends on our ability to explore constrained parameter spaces for finding the best solutions, and more diverse minds and experiences enable us to explore the entire potential solution space more thoroughly, more quickly, and more creatively. With a goal to expand the diversity of experiences and mindsets in our undergraduate bioengineering curricula, Arusha Technical College (ATC) in Arusha, Tanzania and Clemson University (CU) in Clemson, South Carolina, U.S., have partnered together over the past 5 years to provide intercontinental educational opportunities for undergraduate students, graduate assistants, and faculty. In 2018, CU and ATC collaborated on an international design course targeting undergraduate students in biomedical engineering focused on global health solutions for resource poor communities. Undergraduate students from ATC and CU collaborated on design projects through formal videoconferenced group meetings, e-mail, and various social media platforms. The year ended with a joint design symposium in Arusha where the students presented on their work in a public poster forum. This successful ATC-CU Global Health Design Collaboration pilot year provides a solid model upon which to build. Students reported overall positive experiences and plans to continue in their curriculum to graduation, as well as some ATC and CU students changing their career direction to include global health initiatives.

Emergence of Collagen Orientation Heterogeneity in Healing Infarcts and an Agent-Based Model.

Spatial heterogeneity of matrix structure can be an important determinant of tissue function. Although bulk properties of collagen structure in healing myocardial infarcts have been characterized previously, regional heterogeneity in infarct structure has received minimal attention. Herein, we quantified regional variations of collagen and nuclear orientations over the initial weeks of healing after infarction in rats, and employed a computational model of infarct remodeling to test potential explanations for the heterogeneity we observed in vivo. Fiber and cell orientation maps were generated from infarct samples acquired previously at 1, 2, 3, and 6 weeks postinfarction in a rat ligation model. We analyzed heterogeneity by calculating the dot product of each fiber or cell orientation vector with every other fiber or cell orientation vector, and plotting that dot product versus distance between the fibers or cells. This analysis revealed prominent regional heterogeneity, with alignment of both fibers and cell nuclei in local pockets far exceeding the global average. Using an agent-based model of fibroblast-mediated collagen remodeling, we found that similar levels of heterogeneity can spontaneously emerge from initially isotropic matrix via locally reinforcing cell-matrix interactions. Specifically, cells that sensed fiber orientation at a distance or remodeled fibers at a distance by traction-mediated reorientation or aligned deposition gave rise to regionally heterogeneous structures. However, only the simulations in which cells deposited collagen fibers aligned with their own orientation reproduced experimentally measured patterns of heterogeneity across all time points. These predictions warrant experimental follow-up to test the role of such mechanisms in vivo and identify opportunities to control heterogeneity for therapeutic benefit.

Modifying the mechanics of healing infarcts: Is better the enemy of good?

Myocardial infarction (MI) is a major source of morbidity and mortality worldwide, with over 7 million people suffering infarctions each year. Heart muscle damaged during MI is replaced by a collagenous scar over a period of several weeks, and the mechanical properties of that scar tissue are a key determinant of serious post-MI complications such as infarct rupture, depression of heart function, and progression to heart failure. Thus, there is increasing interest in developing therapies that modify the structure and mechanics of healing infarct scar. Yet most prior attempts at therapeutic scar modification have failed, some catastrophically. This article reviews available information about the mechanics of healing infarct scar and the functional impact of scar mechanical properties, and attempts to infer principles that can better guide future attempts to modify scar. One important conclusion is that collagen structure, mechanics, and remodeling of healing infarct scar vary so widely among experimental models that any novel therapy should be tested across a range of species, infarct locations, and reperfusion protocols. Another lesson from past work is that the biology and mechanics of healing infarcts are sufficiently complex that the effects of interventions are often counterintuitive; for example, increasing infarct stiffness has little effect on heart function, and inhibition of matrix metalloproteases (MMPs) has little effect on scar collagen content. Computational models can help explain such counterintuitive results, and are becoming an increasingly important tool for integrating known information to better identify promising therapies and design experiments to test them. Moving forward, potentially exciting new opportunities for therapeutic modification of infarct mechanics include modulating anisotropy and promoting scar compaction.

Computational modeling of cardiac fibroblasts and fibrosis.

Altered fibroblast behavior can lead to pathologic changes in the heart such as arrhythmia, diastolic dysfunction, and systolic dysfunction. Computational models are increasingly used as a tool to identify potential mechanisms driving a phenotype or potential therapeutic targets against an unwanted phenotype. Here we review how computational models incorporating cardiac fibroblasts have clarified the role for these cells in electrical conduction and tissue remodeling in the heart. Models of fibroblast signaling networks have primarily focused on fibroblast cell lines or fibroblasts from other tissues rather than cardiac fibroblasts, specifically, but they are useful for understanding how fundamental signaling pathways control fibroblast phenotype. In the future, modeling cardiac fibroblast signaling, incorporating -omics and drug-interaction data into signaling network models, and utilizing multi-scale models will improve the ability of in silico studies to predict potential therapeutic targets against adverse cardiac fibroblast activity.

Parameter sensitivity analysis of a lumped-parameter model of a chain of lymphangions in series.

Any disruption of the lymphatic system due to trauma or injury can lead to edema. There is no effective cure for lymphedema, partly because predictive knowledge of lymphatic system reactions to interventions is lacking. A well-developed model of the system could greatly improve our understanding of its function. Lymphangions, defined as the vessel segment between two valves, are the individual pumping units. Based on our previous lumped-parameter model of a chain of lymphangions, this study aimed to identify the parameters that affect the system output the most using a sensitivity analysis. The system was highly sensitive to minimum valve resistance, such that variations in this parameter caused an order-of-magnitude change in time-average flow rate for certain values of imposed pressure difference. Average flow rate doubled when contraction frequency was increased within its physiological range. Optimum lymphangion length was found to be some 13-14.5 diameters. A peak of time-average flow rate occurred when transmural pressure was such that the pressure-diameter loop for active contractions was centered near maximum passive vessel compliance. Increasing the number of lymphangions in the chain improved the pumping in the presence of larger adverse pressure differences. For a given pressure difference, the optimal number of lymphangions increased with the total vessel length. These results indicate that further experiments to estimate valve resistance more accurately are necessary. The existence of an optimal value of transmural pressure may provide additional guidelines for increasing pumping in areas affected by edema.

Risk factors for infection after orthopaedic spine surgery at a high-volume institution.

Surgical site infections are associated with increased morbidity, mortality, and resource utilization. To identify risk factors for infection, the authors reviewed all orthopaedic spine operations at Duke University Medical Center from 2005 to 2010. Of the 3138 patients treated during the study period, 115 developed a surgical site infection (3.7%). Demographics, comorbidities, and perioperative blood glucose levels were analyzed in the infected and uninfected cohorts. History of myocardial infarction, congestive heart failure, renal disease, pneumonia, urinary tract infection, and diabetes mellitus (DM) were associated with a higher risk of infection. Notably, a diagnosis of DM nearly doubled the risk of infection. Even in patients without DM, perioperative blood glucose levels greater than 140 mg/dL doubled the risk of infection. The authors propose that strict blood glucose control in both DM and non-DM patients may significantly reduce the risk of infection after spinal surgery.

Computational Screen for Sex-Specific Drug Effects in a Cardiac Fibroblast Network Model.

Heart disease is the leading cause of death in both men and women. Cardiac fibrosis is the uncontrolled accumulation of extracellular matrix proteins which can exacerbate the progression of heart failure, and there are currently no drugs approved specifically to target matrix accumulation in the heart. Computational signaling network models (SNMs) can be used to facilitate discovery of novel drug targets. However, the vast majority of SNMs are not sex-specific and/or are developed and validated using data skewed towards male in vitro and in vivo samples. Biological sex is an important consideration in cardiovascular health and drug development. In this study, we integrate a previously constructed cardiac fibroblast SNM with estrogen signaling pathways to create sex-specific SNMs. The sex-specific SNMs maintained previously high validation when compared to in vitro experimental studies in the literature. A sex-specific perturbation analysis and drug screen uncovered several potential pathways that warrant further study in the pursuit of sex-specific treatment recommendations for cardiac fibrosis.

Computational screen for sex-specific drug effects in a cardiac fibroblast signaling network model.

Heart disease is the leading cause of death in both men and women. Cardiac fibrosis is the uncontrolled accumulation of extracellular matrix proteins, which can exacerbate the progression of heart failure, and there are currently no drugs approved specifically to target matrix accumulation in the heart. Computational signaling network models (SNMs) can be used to facilitate discovery of novel drug targets. However, the vast majority of SNMs are not sex-specific and/or are developed and validated using data skewed towards male in vitro and in vivo samples. Biological sex is an important consideration in cardiovascular health and drug development. In this study, we integrate a cardiac fibroblast SNM with estrogen signaling pathways to create sex-specific SNMs. The sex-specific SNMs demonstrated high validation accuracy compared to in vitro experimental studies in the literature while also elucidating how estrogen signaling can modulate the effect of fibrotic cytokines via multi-pathway interactions. Further, perturbation analysis and drug screening uncovered several drug compounds predicted to generate divergent fibrotic responses in male vs. female conditions, which warrant further study in the pursuit of sex-specific treatment recommendations for cardiac fibrosis. Future model development and validation will require more generation of sex-specific data to further enhance modeling capabilities for clinically relevant sex-specific predictions of cardiac fibrosis and treatment.

Mechanical strain modulates extracellular matrix degradation and byproducts in an isoform-specific manner.

Many studies have shown that mechanical forces can alter collagen degradation by proteases, and this mechanochemical effect may potentially serve an important role in determining extracellular matrix content and organization in load-bearing tissues. However, it is not yet known whether mechano-sensitive degradation depends on particular protease isoforms, nor is it yet known whether particular degradation byproducts can be altered by mechanical loading. In this study, we tested the hypothesis that different types of proteases exhibit different sensitivities to mechanical loading both in degradation rates and byproducts. Decellularized porcine pericardium samples were treated with human recombinant matrix metalloproteinases-1, -8, -9, cathepsin K, or a protease-free control while subjected to different levels of strain in a planar, biaxial mechanical tester. Tissue degradation was monitored by tracking the decay in mechanical stresses during displacement control tests, and byproducts were assessed by mass spectrometry analysis of the sample supernatant after degradation. Our key finding shows that cathepsin K-mediated degradation of collagenous tissue was enhanced with increasing strain, while MMP1-, MMP8-, and MMP9-mediated degradation were first decreased and then increased by strain. Degradation induced changes in tissue mechanical properties, and proteomic analysis revealed strain-sensitive degradome signatures with different ECM byproducts released at low vs. high strains. This evidence suggests a potentially new type of mechanobiology wherein mechanical forces alter the degradation products that can provide important signaling feedback functions during tissue remodeling.

In vitro bioreactor for mechanical control and characterization of tissue constructs.

Cardiac fibrosis is a key contributor to the onset and progression of heart failure and occurs from extracellular matrix accumulation via activated cardiac fibroblasts. Cardiac fibroblasts activate in response to mechanical stress and have been studied in the past by applying forces and deformations to three-dimensional, cell-seeded gels and tissue constructs in vitro. Unfortunately, previous stretching platforms have traditionally not enabled mechanical property assessment to be performed with an efficient throughput, thereby limiting the full potential of in vitro mechanobiology studies. We have developed a novel in vitro platform to study cell-populated tissue constructs under dynamic mechanical stimulation while also performing repeatable, non-destructive stress-strain tests in living constructs. Additionally, this platform can perform these tests across all constructs in a multi-well plate simultaneously, providing exciting potential for direct, functional readouts in future screening applications. In our pilot application, we showed that cyclically stretching cell-populated tissue constructs composed of murine cardiac fibroblasts within a 3D fibrin matrix leads to collagen accumulation and increased tissue stiffness over a three-day time course. Results of this study validate our platform's ability to apply mechanical loads to tissues while performing live mechanical analyses to observe cell-mediated tissue remodeling.

Determination of metarrestin (ML-246) in human plasma for a first-in-human clinical pharmacokinetic application by a simple and efficient uHPLC-MS/MS assay.

Metarrestin is a first-in-class small molecule inhibitor targeting the perinucleolar compartment, a subnuclear body associated with metastatic capacity. Promising preclinical results led to the clinical translation of the compound into a first-in-human phase I trial (NCT04222413). To characterize metarrestin's pharmacokinetic profile in humans, a uHPLC-MS/MS assay was developed and validated to determine the disposition of the drug in human plasma. Efficient sample preparation was accomplished through one-step protein precipitation paired with elution through a phospholipid filtration plate. Chromatographic separation was achieved with gradient elution through an Acuity UPLC® BEH C18 column (50 × 2.1 mm, 1.7 µm). Tandem mass spectrometry facilitated the detection of metarrestin and tolbutamide, the internal standard. The effective calibration range spanned 1-5000 ng/mL and was both accurate (range -5.9 % to 4.9 % deviation) and precise (≤9.0 %CV). Metarrestin proved stable (≤4.9 % degradation) under various assay-imposed conditions. Matrix effects, extraction efficiency, and process efficiency were assessed. Further, the assay was successfully able to determine the disposition of orally administered metarrestin in patients from the lowest dose cohort (1 mg) for 48 h post-administration. Thus, the validated analytical method detailed in this work is simple, sensitive, and clinically applicable.

In Silico Re-Optimization of Atezolizumab Dosing Using Population Pharmacokinetic Simulation and Exposure-Response Simulation.

Atezolizumab, a humanized monoclonal antibody against programmed cell death ligand 1 (PD-L1), was initially approved in 2016, around the same time that the sponsor published the minimum serum concentration to maintain the saturation of receptor occupancy (6 µg/mL). The initially approved dose regimen of 1200 mg every 3 weeks (q3w) was subsequently modified to 840 mg q2w or 1680 mg q4w through pharmacokinetic simulations. Yet, each standard regimen yields steady-state trough concentrations (CMIN,SS ) far exceeding (≈ 40-fold) the stated target concentration. Additionally, the steady-state area under the plasma drug concentration-time curve (AUCSS ) at 1200 mg q3w was significantly (P = .027) correlated with the probability of adverse events of special interest (AESIs) in patients with non-small cell lung cancer (NSCLC) and, coupled with excess exposure, this provides incentive to explore alternative dose regimens to lower the exposure burden while maintaining an effective CMIN,SS . In this study, we first identified 840 mg q6w as an extended-interval regimen that could robustly maintain a serum concentration of 6 µg/mL (≥99% of virtual patients simulated, n = 1000), then applied this regimen to an approach that administers 2 "loading doses" of standard-interval regimens for a future clinical trial aiming to personalize dose regimens. Each standard dose was simulated for 2 loading doses, then 840 mg q6w thereafter; all yielded cycle-7 CMIN,SS values of >6 µg/mL in >99% of virtual patients. Further, the AUCSS from 840 mg q6w resulted in a flattening (P = .63) of the exposure-response relationship with adverse events of special interest (AESIs). We next aim to verify this in a clinical trial seeking to validate extended-interval dosing in a personalized approach using therapeutic drug monitoring.

Osteoimmunology: Interactions With the Immune System in Spinal Fusion.

Spinal fusion is important for the clinical success of patients undergoing surgery, and the immune system plays an increasingly recognized role. Osteoimmunology is the study of the interactions between the immune system and bone. Inflammation impacts the osteogenic, osteoconductive, and osteoinductive properties of bone grafts and substitutes and ultimately influences the success of spinal fusion. Macrophages have emerged as important cells for coordinating the immune response following spinal fusion surgery, and macrophage-derived cytokines impact each phase of bone graft healing. This review explores the cellular and molecular immune processes that regulate bone homeostasis and healing during spinal fusion.

Fibroblast mechanotransduction network predicts targets for mechano-adaptive infarct therapies.

Regional control of fibrosis after myocardial infarction is critical for maintaining structural integrity in the infarct while preventing collagen accumulation in non-infarcted areas. Cardiac fibroblasts modulate matrix turnover in response to biochemical and biomechanical cues, but the complex interactions between signaling pathways confound efforts to develop therapies for regional scar formation. We employed a logic-based ordinary differential equation model of fibroblast mechano-chemo signal transduction to predict matrix protein expression in response to canonical biochemical stimuli and mechanical tension. Functional analysis of mechano-chemo interactions showed extensive pathway crosstalk with tension amplifying, dampening, or reversing responses to biochemical stimuli. Comprehensive drug target screens identified 13 mechano-adaptive therapies that promote matrix accumulation in regions where it is needed and reduce matrix levels in regions where it is not needed. Our predictions suggest that mechano-chemo interactions likely mediate cell behavior across many tissues and demonstrate the utility of multi-pathway signaling networks in discovering therapies for context-specific disease states.

Pharmacokinetics and tolerability of the dual TORC1/2 inhibitor sapanisertib in combination with the MEK inhibitor trametinib in dogs.

Activation of one or both the Ras/MAPK and PI3K/Akt/mTOR signal transduction pathways are known to mediate oncogenicity of several canine and human cancers, including mucosal melanomas. Reciprocal cross activation between the two pathways can be a source of drug resistance. Consequently, oral dosing for plasma pharmacokinetic (PK) analysis and tolerability to a combination of sapanisertib, a dual TORC1/2 inhibitor, and trametinib, a MEK inhibitor, was evaluated in nontumor-bearing laboratory dogs for its potential application in parallel pathway targeting. Twelve dogs, divided into three equal cohorts, received either the combination or single agents. Animals were monitored for PK following single dose and 17-day repeat dosing, and by clinical observations, hematology, serum biochemistry, coagulation studies and urinalyses. A single trametinib dose (0.025 mg/kg), sulfated as dimethyl sulfoxide which enhanced its absorption, reached mean maximum concentration (Cmax) 0.64 ng/mL [18% coefficient of variation (CV)] at a median time to maximum concentration (Tmax) of 1.5 h (hr), and mean area under the concentration-time curve (AUC) 16.8 hr*ng/mL (14%CV), which were similar when given alone or in combination with sapanisertib. A prolonged half-life afforded 3-4-fold plasma accumulation of trametinib with daily dosing, analogous to humans. Trametinib PK mirrored previous regulatory data in dogs, while exposure approximated some published human values but generally not all patients. Sapanisertib-alone in canine plasma following single 0.1 mg/kg dose [mean Cmax 26.3 ng/mL (21%CV), median Tmax 2.0 hr, and mean AUC 248 hr*ng/mL (41%CV)] resembled levels in human therapeutic trials; whereas canine sapanisertib exposure was reduced when combined with trametinib, a known cytochrome P450 CYP3A4 inducer. Sex differences were not observed for either drug. Side effects upon repeat dosing with either or both drugs may include body weight loss, maldigestion, and cutaneous discoloration. The combination was tolerated without dose limiting toxicity, although clinical laboratory analyses revealed drug-induced acute-phase inflammation, proteinuria, and decreased blood reticulocytes, mild changes not necessitating intervention. Short-term results in dogs with this combination would appear to hold translational promise for clinical trial evaluation to target canine and possibly human melanoma, as well as other cancers having one or both signal transduction pathway activations.