University of Southern California
  • Los Angeles, California, United States
Recent publications
A model-based framework is presented to predict monoclonal antibody (mAb) pharmacokinetics (PK) in humans based on in vitro measures of antibody physiochemical properties. A physiologically based pharmacokinetic (PBPK) model is used to explore the predictive potential of 14 in vitro assays designed to measure various antibody physiochemical properties, including nonspecific cell-surface interactions, FcRn binding, thermal stability, hydrophobicity, and self-association. Based on the mean plasma PK time course data of 22 mAbs from humans reported in the literature, we found a significant positive correlation (R = 0.64, p = .0013) between the model parameter representing antibody-specific vascular to endothelial clearance and heparin relative retention time, an in vitro measure of nonspecific binding. We also found that antibody-specific differences in paracellular transport due to convection and diffusion could be partially explained by antibody heparin relative retention time (R = 0.52, p = .012). Other physiochemical properties, including antibody thermal stability, hydrophobicity, cross-interaction and self-association, in and of themselves were not predictive of model-based transport parameters. In contrast to other studies that have reported empirically derived expressions relating in vitro measures of antibody physiochemical properties directly to antibody clearance, the proposed PBPK model-based approach for predicting mAb PK incorporates fundamental mechanisms governing antibody transport and processing, informed by in vitro measures of antibody physiochemical properties, and can be expanded to include more descriptive representations of each of the antibody processing subsystems, as well as other antibody-specific information.
Nontuberculous mycobacterial pulmonary diseases (NTM-PDs) are emerging as global health threats with issues of antibiotic resistance. Accumulating evidence suggests that the gut-lung axis may provide novel candidates for host-directed therapeutics against various infectious diseases. However, little is known about the gut-lung axis in the context of host protective immunity to identify new therapeutics for NTM-PDs. This study was performed to identify gut microbes and metabolites capable of conferring pulmonary immunity to NTM-PDs. Using metabolomics analysis of sera from NTM-PD patients and mouse models, we showed that the levels of l-arginine were decreased in sera from NTM-PD patients and NTM-infected mice. Oral administration of l-arginine significantly enhanced pulmonary antimicrobial activities with the expansion of IFN-γ-producing effector T cells and a shift to microbicidal (M1) macrophages in the lungs of NTM-PD model mice. Mice that received fecal microbiota transplants from l-arginine-treated mice showed increased protective host defense in the lungs against NTM-PD, whereas l-arginine-induced pulmonary host defense was attenuated in mice treated with antibiotics. Using 16S rRNA sequencing, we further showed that l-arginine administration resulted in enrichment of the gut microbiota composition with Bifidobacterium species. Notably, oral treatment with either Bifidobacterium pseudolongum or inosine enhanced antimicrobial pulmonary immune defense against NTM infection, even with multidrug-resistant clinical NTM strains. Our findings indicate that l-arginine-induced gut microbiota remodeling with enrichment of B. pseudolongum boosts pulmonary immune defense against NTM infection by driving the protective gut-lung axis in vivo.
Background Genome-wide association studies (GWAS) have identified multiple common breast cancer susceptibility variants. Many of these variants have differential associations by estrogen receptor (ER) status, but how these variants relate with other tumor features and intrinsic molecular subtypes is unclear. Methods Among 106,571 invasive breast cancer cases and 95,762 controls of European ancestry with data on 173 breast cancer variants identified in previous GWAS, we used novel two-stage polytomous logistic regression models to evaluate variants in relation to multiple tumor features (ER, progesterone receptor (PR), human epidermal growth factor receptor 2 (HER2) and grade) adjusting for each other, and to intrinsic-like subtypes. Results Eighty-five of 173 variants were associated with at least one tumor feature (false discovery rate < 5%), most commonly ER and grade, followed by PR and HER2. Models for intrinsic-like subtypes found nearly all of these variants (83 of 85) associated at p < 0.05 with risk for at least one luminal-like subtype, and approximately half (41 of 85) of the variants were associated with risk of at least one non-luminal subtype, including 32 variants associated with triple-negative (TN) disease. Ten variants were associated with risk of all subtypes in different magnitude. Five variants were associated with risk of luminal A-like and TN subtypes in opposite directions. Conclusion This report demonstrates a high level of complexity in the etiology heterogeneity of breast cancer susceptibility variants and can inform investigations of subtype-specific risk prediction.
Background: Shoe contact allergy can be difficult to diagnose and manage. Objective: The aim of the study was to characterize demographics, clinical characteristics, patch test results, and occupational data for the North American Contact Dermatitis Group patients with shoe contact allergy. Methods: This is a retrospective study of 33,661 patients, patch tested from 2005 to 2018, with a shoe source, foot as 1 of 3 sites of dermatitis, and final primary diagnosis of allergic contact dermatitis. Results: Three hundred fifty-two patients met the inclusion criteria. They were more likely to be male (odds ratio = 3.36, confidence interval = 2.71-4.17) and less likely to be older than 40 years (odds ratio = 0.49, confidence interval = 0.40-0.61) compared with others with positive patch test reactions. The most common relevant North American Contact Dermatitis Group screening allergens were potassium dichromate (29.8%), p-tert-butylphenol formaldehyde resin (20.1%), thiuram mix (13.3%), mixed dialkyl thioureas (12.6%), and carba mix (12%). A total of 29.8% (105/352) had positive patch test reactions to supplemental allergens, and 12.2% (43/352) only had reactions to supplemental allergens. Conclusions: Shoe contact allergy was more common in younger and male patients. Potassium dichromate and p-tert-butylphenol formaldehyde resin were the top shoe allergens. Testing supplemental allergens, personal care products, and shoe components should be part of a comprehensive evaluation of suspected shoe contact allergy.
We present a strategy for the design of ferromagnetic materials with exceptionally low magnetic hysteresis, quantified by coercivity. In this strategy, we use a micromagnetic algorithm that we have developed in previous research and which has been validated by its success in solving the “Permalloy Problem”—the well-known difficulty of predicting the composition 78.5% Ni of the lowest coercivity in the Fe–Ni system—and by the insight it provides into the “Coercivity Paradox” of W. F. Brown. Unexpectedly, the design strategy predicts that cubic materials with large saturation magnetization m s and large magnetocrystalline anisotropy constant κ 1 will have low coercivity on the order of that of Permalloy, as long as the magnetostriction constants λ 100 , λ 111 are tuned to special values. The explicit prediction for a cubic material with low coercivity is the dimensionless number $$({c}_{11}-{c}_{12}){\lambda }_{100}^{2}/(2{\kappa }_{1})=81$$ ( c 11 − c 12 ) λ 100 2 / ( 2 κ 1 ) = 81 for 〈100〉 easy axes. The results would seem to have broad potential application, especially to magnetic materials of interest in energy research.
Migraine is a ubiquitous neurologic disease that afflicts people of all ages. Its molecular pathogenesis involves peptides that promote intracranial vasodilation and modulate nociceptive transmission upon release from sensory afferents of cells in the trigeminal ganglion and parasympathetic efferents of cells in the sphenopalatine ganglion. Experimental data have confirmed that intravenous infusion of these vasoactive peptides induce migraine attacks in people with migraine, but it remains a point of scientific contention whether their site of action lies outside or within the central nervous system. In this context, it has been hypothesized that transient dysfunction of brain barriers before or during migraine attacks might facilitate the passage of migraine-inducing peptides into the central nervous system. Here, we review evidence suggestive of brain barrier dysfunction in migraine pathogenesis and conclude with lessons learned in order to provide directions for future research efforts.
Background During embryogenesis lateral symmetry is broken, giving rise to Left/Right (L/R) breast tissues with distinct identity. L/R-sided breast tumors exhibit consistently-biased incidence, gene expression, and DNA methylation. We postulate that a differential L/R tumor-microenvironment crosstalk generates different tumorigenesis mechanisms. Methods We performed in-silico analyses on breast tumors of public datasets, developed xenografted tumors, and conditioned MDA-MB-231 cells with L/R mammary extracts. Results We found L/R differential DNA methylation involved in embryogenic and neuron-like functions. Focusing on ion-channels, we discovered significant L/R epigenetic and bioelectric differences. Specifically, L-sided cells presented increased methylation of hyperpolarizing ion channel genes and increased Ca ²⁺ concentration and depolarized membrane potential, compared to R-ones. Functional consequences were associated with increased proliferation in left tumors, assessed by KI67 expression and mitotic count. Conclusions Our findings reveal considerable L/R asymmetry in cancer processes, and suggest specific L/R epigenetic and bioelectric differences as future targets for cancer therapeutic approaches in the breast and many other paired organs.
Metastatic breast cancer (mBC) patients have a high risk of progression and face poor prognosis overall, with about one third (34%) surviving five years or more. In rare instances (2–4% of cases) patients with mBC have ERBB2 (HER2) activating mutations but are ERBB2 non-amplified. Neratinib is a potent, irreversible inhibitor that binds HER2 and inhibits downstream signaling. We used the previously validated high-definition single cell assay (HDSCA) workflow to investigate the clinical significance of the liquid biopsy in ERBB2 mutant, non-amplified, post-menopausal mBC patients starting neratinib and fulvestrant combination therapy. Characterization with a comprehensive liquid biopsy methodology (HDSCA) included genomic analysis of both the cell-free DNA (cfDNA) and single circulating tumor cells (CTCs) to monitor tumor evolution and identify potential mutational variants unique to the patient’s clinical response. A limited series of five sequentially enrolled patients presented here were from the MutHER ( , NCT01670877) or SUMMIT ( , NCT01953926) trials. Patients had an average of 5.4 lines of therapy before enrollment, variable hormone receptor status, and ERBB2 mutations at diagnosis and during treatment. CTC enumeration alone was not sufficient to predict clinical response. Treatment pressure was shown to lead to an observable change in CTC morphology and genomic instability (GI), suggesting these parameters may inform prognosis. Single cell copy number alteration (CNA) analysis indicated that the persistence or development of a clonal population of CTCs during treatment was associated with a worse response. Hierarchical clustering analysis of the single cells across all patients and timepoints identified distinct aberrant regions shared among patients, comprised of 26 genes that are similarly affected and may be related to drug resistance. Additionally, the genomic analysis of the cfDNA, identified new mutations in ERBB2 , PIK3CA , and TP53 that arose likely due to treatment pressure in a patient with poor response, further providing insights on the dynamics of the cancer genome over the course of therapy. The data presented in this small cohort study demonstrates the feasibility of real-time molecular profiling of the cellular and acellular fractions of the liquid biopsy using the HDSCA methodology. Additional studies are necessary to determine the potential use of morphometric and genomic analysis as a prognostic tool to advance personalized oncology.
Ecological network analyses are used to identify potential biotic interactions between microorganisms from species abundance data. These analyses are often carried out using time-series data; however, time-series networks have unique statistical challenges. Time-dependent species abundance data can lead to species co-occurrence patterns that are not a result of direct, biotic associations and may therefore result in inaccurate network predictions. Here, we describe a generalize additive model (GAM)-based data transformation that removes time-series signals from species abundance data prior to running network analyses. Validation of the transformation was carried out by generating mock, time-series datasets, with an underlying covariance structure, running network analyses on these datasets with and without our GAM transformation, and comparing the network outputs to the known covariance structure of the simulated data. The results revealed that seasonal abundance patterns substantially decreased the accuracy of the inferred networks. In addition, the GAM transformation increased the predictive power (F1 score) of inferred ecological networks on average and improved the ability of network inference methods to capture important features of network structure. This study underscores the importance of considering temporal features when carrying out network analyses and describes a simple, effective tool that can be used to improve results.
Background Musculoskeletal modeling is currently a preferred method for estimating the muscle forces that underlie observed movements. However, these estimates are sensitive to a variety of assumptions and uncertainties, which creates difficulty when trying to interpret the muscle forces from musculoskeletal simulations. Here, we describe an approach that uses Bayesian inference to identify plausible ranges of muscle forces for a simple motion while representing uncertainty in the measurement of the motion and the objective function used to solve the muscle redundancy problem. Methods We generated a reference elbow flexion–extension motion and computed a set of reference forces that would produce the motion while minimizing muscle excitations cubed via OpenSim Moco. We then used a Markov Chain Monte Carlo (MCMC) algorithm to sample from a posterior probability distribution of muscle excitations that would result in the reference elbow motion. We constructed a prior over the excitation parameters which down-weighted regions of the parameter space with greater muscle excitations. We used muscle excitations to find the corresponding kinematics using OpenSim, where the error in position and velocity trajectories (likelihood function) was combined with the sum of the cubed muscle excitations integrated over time (prior function) to compute the posterior probability density. Results We evaluated the muscle forces that resulted from the set of excitations that were visited in the MCMC chain (seven parallel chains, 500,000 iterations per chain). The estimated muscle forces compared favorably with the reference forces generated with OpenSim Moco, while the elbow angle and velocity from MCMC matched closely with the reference (average RMSE for elbow angle = 2°; and angular velocity = 32°/s). However, our rank plot analyses and potential scale reduction statistics, which we used to evaluate convergence of the algorithm, indicated that the chains did not fully mix. Conclusions While the results from this process are a promising step towards characterizing uncertainty in muscle force estimation, the computational time required to search the solution space with, and the lack of MCMC convergence indicates that further developments in MCMC algorithms are necessary for this process to become feasible for larger-scale models.
The recent National Institute of Health (NIH) INCLUDE (INvestigation of Co-occurring conditions across the Lifespan to Understand Down syndromE) initiative has bolstered capacity for the current increase in clinical trials involving individuals with Down syndrome (DS). This new NIH funding mechanism offers new opportunities to expand and develop novel approaches in engaging and effectively enrolling a broader representation of clinical trials participants addressing current medical issues faced by individuals with DS. To address this opportunity, the NIH assembled leading clinicians, scientists, and representatives of advocacy groups to review existing methods and to identify those areas where new approaches are needed to engage and prepare DS populations for participation in clinical trial research. This paper summarizes the results of the Clinical Trial Readiness Working Group that was part of the INCLUDE Project Workshop: Planning a Virtual Down Syndrome Cohort Across the Lifespan Workshop held virtually September 23 and 24, 2019.
Personalized medicine promises individualized disease prediction and treatment. The convergence of machine learning (ML) and available multimodal data is key moving forward. We build upon previous work to deliver multimodal predictions of Parkinson’s disease (PD) risk and systematically develop a model using GenoML, an automated ML package, to make improved multi-omic predictions of PD, validated in an external cohort. We investigated top features, constructed hypothesis-free disease-relevant networks, and investigated drug–gene interactions. We performed automated ML on multimodal data from the Parkinson’s progression marker initiative (PPMI). After selecting the best performing algorithm, all PPMI data was used to tune the selected model. The model was validated in the Parkinson’s Disease Biomarker Program (PDBP) dataset. Our initial model showed an area under the curve (AUC) of 89.72% for the diagnosis of PD. The tuned model was then tested for validation on external data (PDBP, AUC 85.03%). Optimizing thresholds for classification increased the diagnosis prediction accuracy and other metrics. Finally, networks were built to identify gene communities specific to PD. Combining data modalities outperforms the single biomarker paradigm. UPSIT and PRS contributed most to the predictive power of the model, but the accuracy of these are supplemented by many smaller effect transcripts and risk SNPs. Our model is best suited to identifying large groups of individuals to monitor within a health registry or biobank to prioritize for further testing. This approach allows complex predictive models to be reproducible and accessible to the community, with the package, code, and results publicly available.
Microfluidic organ-on-a-chip technologies have enabled construction of biomimetic physiologically and pathologically relevant models. This paper describes an injection molded microfluidic platform that utilizes a novel sequential edge-guided patterning method based on spontaneous capillary flow to realize three-dimensional co-culture models and form an array of micro-vascularized tissues (28 per 1 × 2-inch slide format). The MicroVascular Injection-Molded Plastic Array 3D Culture (MV-IMPACT) platform is fabricated by injection molding, resulting in devices that are reliable and easy to use. By patterning hydrogels containing human umbilical endothelial cells and fibroblasts in close proximity and allowing them to form vasculogenic networks, an array of perfusable vascularized micro-tissues can be formed in a highly efficient manner. The high-throughput generation of angiogenic sprouts was quantified and their uniformity was characterized. Due to its compact design (half the size of a 96-well microtiter plate), it requires small amount of reagents and cells per device. In addition, the device design is compatible with a high content imaging machine such as Yokogawa CQ-1. Furthermore, we demonstrated the potential of our platform for high-throughput phenotypic screening by testing the effect of DAPT, a chemical known to affect angiogenesis. The MV-IMPACT represent a significant improvement over our previous PDMS-based devices in terms of molding 3D co-culture conditions at much higher throughput with added reliability and robustness in obtaining vascular micro-tissues and will provide a platform for developing applications in drug screening and development.
Background The discovery of the prominent action of Calcitonin Gene Related Peptide –CGRP- on trigeminal afferents and meningeal vessels, opened a new era in migraine treatment. However, how the block of nociceptive afferents could act on central mechanisms of migraine is still not clear. In this pilot study we aimed to test the effect of 3 months Galcanezumab (CGA) therapy on occipital visual reactivity in migraine patients, using the Steady State Visual Evoked Potentials-SSVEPs and Functional Near Infrared Spectroscopy –fNIRS. Method Thirteen migraine patients underwent clinical and neurophysiological examination in basal condition (T0), 1 h after GCA injection (T1) and after 3 months of GCA treatment (T2). Ten healthy volunteers were also evaluated. Results At T2, there was a reduction of headache frequency and disability. At T2, the EEG power significantly diminished as compared to T0 and T1 at occipital sites, and the topographical analysis confirmed a restoration of SSVEPs within normal values. The Oxyhemoglobin levels in occipital cortex, which were basically increased during visual stimulation in migraine patients, reverted to normal values at T2. Conclusions The present pilot study indicates that Galcanezumab could act on cortical targets located beyond the pain network, restoring the abnormal occipital reactivity. This effect could indicate the possible disease modifying properties of CGRP related monoclonal antibodies.
Background Cigarette smoke is a major public health concern. Epigenetic aging may be an important pathway by which exposure to cigarette smoke affects health. However, little is known about how exposure to smoke at different life stages affects epigenetic aging, especially in older adults. This study examines how three epigenetic aging measures (GrimAge, PhenoAge, and DunedinPoAm38) are associated with parental smoking, smoking in youth, and smoking in adulthood, and whether these epigenetic aging measures mediate the link between smoke exposure and morbidity and mortality. This study utilizes data from the Health and Retirement Study (HRS) Venous Blood Study (VBS), a nationally representative sample of US adults over 50 years old collected in 2016. 2978 participants with data on exposure to smoking, morbidity, and mortality were included. Results GrimAge is significantly increased by having two smoking parents, smoking in youth, and cigarette pack years in adulthood. PhenoAge and DunedinPoAm38 are associated with pack years. All three mediate some of the effect of pack years on cancer, high blood pressure, heart disease, and mortality and GrimAge and DunedinPoAm38 mediate this association on lung disease. Conclusions Results suggest epigenetic aging is one biological mechanism linking lifetime exposure to smoking with development of disease and earlier death in later life. Interventions aimed at reducing smoking in adulthood may be effective at weakening this association.
Background Down syndrome regression disorder is a symptom cluster consisting of neuropsychiatric regression without cause. This study evaluated the incidence of neurodiagnostic abnormalities in individuals with Down syndrome regression disorder and determined if abnormalities are indicative of responses to therapeutic intervention. Methods A retrospective, multi-center, case-control study was performed. Patients were required to have subacute onset and the presence of four of five symptom groups present (cognitive decline, expressive language, sleep derangement, loss of ability to perform activities of daily living, and/or a new movement disorder) and no other explanation for symptoms. Results Individuals with Down syndrome regression disorder were comparable to a cohort of individuals with only Down syndrome although had higher rates of autoimmune disease ( p = 0.02, 95%CI 1.04–1.75). Neurodiagnostic abnormalities were found on EEG ( n = 19, 26%), neuroimaging ( n = 16, 22%), and CSF ( n = 9, 17%). Pleocytosis was appreciated in five cases, elevated total protein in nine, elevated IgG index in seven, and oligoclonal bands in two. Testing within 2 years of symptom onset was more likely to have neurodiagnostic abnormalities ( p = 0.01, 95%CI 1.64–37.06). In individuals with neurodiagnostic abnormalities, immunotherapy was nearly four times more likely to have a therapeutic effect than in those without neurodiagnostic abnormalities (OR 4.11, 95%CI 1.88–9.02). In those with normal neurodiagnostic studies ( n = 43), IVIg was effective in 14 of 17 (82%) patients as well although other immunotherapies were uniformly ineffective. Conclusions This study reports the novel presence of neurodiagnostic testing abnormalities in individuals with Down syndrome regression disorder, providing credence to this symptom cluster potentially being of neurologic and/or neuroimmunologic etiology.
Suicides are preventable tragedies, if risk factors are tracked and mitigated. We had previously developed a new quantitative suicidality risk assessment instrument (Convergent Functional Information for Suicidality, CFI-S), which is in essence a simple polyphenic risk score, and deployed it in a busy urban hospital Emergency Department, in a naturalistic cohort of consecutive patients. We report a four years follow-up of that population (n = 482). Overall, the single administration of the CFI-S was significantly predictive of suicidality over the ensuing 4 years (occurrence- ROC AUC 80%, severity- Pearson correlation 0.44, imminence-Cox regression Hazard Ratio 1.33). The best predictive single phenes (phenotypic items) were feeling useless (not needed), a past history of suicidality, and social isolation. We next used machine learning approaches to enhance the predictive ability of CFI-S. We divided the population into a discovery cohort (n = 255) and testing cohort (n = 227), and developed a deep neural network algorithm that showed increased accuracy for predicting risk of future suicidality (increasing the ROC AUC from 80 to 90%), as well as a similarity network classifier for visualizing patient's risk. We propose that the widespread use of CFI-S for screening purposes, with or without machine learning enhancements, can boost suicidality prevention efforts. This study also identified as top risk factors for suicidality addressable social determinants. Supplementary information: The online version contains supplementary material available at 10.1007/s44192-022-00016-z.
Background Pregaming is a high-drink context popular among college students that often leads to elevated blood alcohol levels and negative consequences. Over 15 years of research studies have demonstrated that pregaming represents one of the riskiest known behaviors among college students, yet no pregaming-specific interventions have been developed to help prevent this behavior. General brief interventions for students do not reduce pregaming behavior and may not be appropriate, as they do not help students develop skills unique to the pregaming context that could help them drink less. We developed a brief, mobile-based intervention that is proposed to prevent heavy drinking during pregaming for college students, with the ultimate goal that behavioral reductions in this risky practice will ultimately affect global drinking and prevent consequences. Methods/Design The intervention, Pregaming Awareness in College Environments (PACE), was developed by combining two innovations to facilitate behavior change: (1) a mobile-based application that increases accessibility, is easy and engaging to use, and broadens the reach of the intervention content and (2) personalized pregaming-specific intervention content with harm reduction and cognitive behavioral skills proven to be mechanisms preventing and reducing heavy drinking among college students. After a develop and beta-test phase, we propose to test the efficacy of PACE in a preliminary randomized controlled trial with 500 college students who pregame at least once per week. Pregaming, general drinking, and alcohol-related consequences outcomes will be examined in the immediate (2 weeks post-intervention) and short-terms (six and 14-week post-intervention). We will also evaluate moderator effects for age, sex, and heaviness of drinking to allow for more refined information for a planned larger test of the intervention to follow this initial trial of PACE. Discussion This pregaming intervention clinical trial, if found to be efficacious, will culminate with an easily-disseminated mobile-based intervention for college student drinkers. It has the potential to reach millions of college students, perhaps as a clinical tool used by college counseling centers as an adjunct to formal care or as a preventive tool for first-year students or other high-risk groups on campus. Trial registration : Identifier NCT04016766.
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22,927 members
Dominique Duncan
  • Institute for Neuroimaging and Informatics (INI)
Gully Burns
  • Information Sciences Institute
Daryl L Davies
  • Titus Family Department of Clinical Pharmacy
Titus Galama
  • Center for Economic and Social Research
Antonio Ortega
  • Department of Electrical and Computer Engineering
University Park Campus, 90089, Los Angeles, California, United States
Head of institution
Carol Lynn Folt