Simulating the Simultaneous Impact of Medication for Opioid Use Disorder and Naloxone on Opioid Overdose Death in Eight New York Counties.

BACKGROUND: The United States is in the midst of an opioid overdose epidemic; 28.3 per 100,000 people died of opioid overdose in 2020. Simulation models can help understand and address this complex, dynamic, and nonlinear social phenomenon. Using the HEALing Communities Study, aimed at reducing opioid overdoses, and an agent-based model, Simulation of Community-Level Overdose Prevention Strategy, we simulated increases in buprenorphine initiation and retention and naloxone distribution aimed at reducing overdose deaths by 40% in New York Counties. METHODS: Our simulations covered 2020-2022. The eight counties contrasted urban or rural and high and low baseline rates of opioid use disorder treatment. The model calibrated agent characteristics for opioid use and use disorder, treatments and treatment access, and fatal and nonfatal overdose. Modeled interventions included increased buprenorphine initiation and retention, and naloxone distribution. We predicted a decrease in the rate of fatal opioid overdose 1 year after intervention, given various modeled intervention scenarios. RESULTS: Counties required unique combinations of modeled interventions to achieve a 40% reduction in overdose deaths. Assuming a 200% increase in naloxone from current levels, high baseline treatment counties achieved a 40% reduction in overdose deaths with a simultaneous 150% increase in buprenorphine initiation. In comparison, low baseline treatment counties required 250-300% increases in buprenorphine initiation coupled with 200-1000% increases in naloxone, depending on the county. CONCLUSIONS: Results demonstrate the need for tailored county-level interventions to increase service utilization and reduce overdose deaths, as the modeled impact of interventions depended on the county's experience with past and current interventions.

Growth and dormancy control of myeloma cells by mesenchymal stem cells.

Bone marrow mesenchymal stem cells (MSCs) may have contrasting impacts on the progression of multiple myeloma (MM). Priming normal MSCs, by culturing them with MM cells, mimics the MSC-induced MM growth. We studied the contrasting effects of conditioned medium (CM) from unprimed or primed MSCs on growth of MM cells from newly diagnosed cases. We elucidated potential molecular pathways using global gene expression profiling and focused on the role of the mTOR2 component, RICTOR, as a novel mediator of dormancy in MM. Primed MSCs CM consistently increased proportions of proliferating cells and supported MM growth in 3-day (n = 20) and 10-day (n = 12) cultures, effects that were partially mediated through the IGF1 axis. In contrast, unprimed MSCs CM inhibited growth of MM cells in cases mainly from stages I/II MM. The genes most overexpressed in MM cells treated with primed MSCs CM were associated with cell cycle, DNA-damage repair, and proliferation; genes most overexpressed in MM cells treated with unprimed MSCs CM were associated with dormancy pathways including RICTOR (mTOR2 pathway), CXCR4, and BCL2. RICTOR protein level was induced by unprimed MSCs CM and was lower in KI67+ proliferating MM cells treated with primed MSCs CM. RICTOR was underexpressed in clinical relapse samples compared with baseline samples of the same patients. Inhibiting RICTOR expression in primary MM cells promoted their growth, and enforced expression of RICTOR in MM cell lines inhibited their growth. Our findings suggest that, after prolonged interactions with MM cells, bone marrow MSCs shift from MM-repressive to MM-permissive. AVAILABILITY OF DATA AND MATERIALS: Our institutional GEP data of MM cells from newly diagnosed patients used to show RICTOR expression have been deposited at Gene Expression Omnibus (GEO: GSE2658, https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE2658).

Inverse Generative Social Science: Backward to the Future.

The agent-based model is the principal scientific instrument of generative social science. Typically, we design completed agents-fully endowed with rules and parameters-to grow macroscopic target patterns from the bottom up. Inverse generative science (iGSS) stands this approach on its head: Rather than handcrafting completed agents to grow a target-the forward problem-we start with the macro-target and evolve micro-agents that generate it, stipulating only primitive agent-rule constituents and permissible combinators. Rather than specific agents as designed inputs, we are interested in agents-indeed, families of agents-as evolved outputs. This is the backward problem and tools from Evolutionary Computing can help us solve it. In this overarching essay of the current JASSS Special Section, Part 1 discusses the motivation for iGSS. Part 2 discusses its goals, as distinct from other approaches. Part 3 discusses how to do it concretely, previewing the five iGSS applications that follow. Part 4 discusses several foundational issues for agent-based modeling and economics. Part 5 proposes a central future application of iGSS: to evolve explicit formal alternatives to the Rational Actor, with Agent_Zero as one possible point of evolutionary departure. Conclusions and future research directions are offered in Part 6. Looking 'backward to the future,' I also include, as Appendices, a pair of 1992 memoranda to the then President of the Santa Fe Institute on the forward (growing artificial societies from the bottom up) and backward (iGSS) problems.

Can Social Norms Explain Long-Term Trends in Alcohol Use? Insights from Inverse Generative Social Science.

Social psychological theory posits entities and mechanisms that attempt to explain observable differences in behavior. For example, dual process theory suggests that an agent's behavior is influenced by intentional (arising from reasoning involving attitudes and perceived norms) and unintentional (i.e., habitual) processes. In order to pass the generative sufficiency test as an explanation of alcohol use, we argue that the theory should be able to explain notable patterns in alcohol use that exist in the population, e.g., the distinct differences in drinking prevalence and average quantities consumed by males and females. In this study, we further develop and apply inverse generative social science (iGSS) methods to an existing agent-based model of dual process theory of alcohol use. Using iGSS, implemented within a multi-objective grammar-based genetic program, we search through the space of model structures to identify whether a single parsimonious model can best explain both male and female drinking, or whether separate and more complex models are needed. Focusing on alcohol use trends in New York State, we identify an interpretable model structure that achieves high goodness-of-fit for both male and female drinking patterns simultaneously, and which also validates successfully against reserved trend data. This structure offers a novel interpretation of the role of norms in formulating drinking intentions, but the structure's theoretical validity is questioned by its suggestion that individuals with low autonomy would act against perceived descriptive norms. Improved evidence on the distribution of autonomy in the population is needed to understand whether this finding is substantive or is a modeling artefact.

Privacy and contact tracing efficacy.

As the COVID-19 pandemic emerged, public health authorities and software designers considered the possibility that smartphones could be used for contact tracing to control disease spread. Smartphone-based contact tracing was attractive in part because it promised to allow the tracing of contacts that might not be reported using traditional contact tracing methods. Comprehensive contact tracing raises distinctive privacy concerns, however, that have not been previously explored. Contacts outside of an individual's ordinary social network are more likely to be privacy-sensitive, making fear that such contacts will be disclosed a potential disincentive to adoption of smartphone contact tracing. Here, we modify the standard SEIR infectious disease transmission model to incorporate contact tracing and perform a series of simulations aimed at studying the importance of tracing socially distant (and potentially privacy-sensitive) contacts. We find that, for a simple model network, ensuring that distant contacts are traced is surprisingly unimportant as long as contact tracing adoption is sufficiently high. These results suggest that policy-makers designing contact tracing systems should be willing to trade off comprehensiveness for more widespread adoption.

Triple contagion: a two-fears epidemic model.

We present a differential equations model in which contagious disease transmission is affected by contagious fear of the disease and contagious fear of the control, in this case vaccine. The three contagions are coupled. The two fears evolve and interact in ways that shape distancing behaviour, vaccine uptake, and their relaxation. These behavioural dynamics in turn can amplify or suppress disease transmission, which feeds back to affect behaviour. The model reveals several coupled contagion mechanisms for multiple epidemic waves. Methodologically, the paper advances infectious disease modelling by including human behavioural adaptation, drawing on the neuroscience of fear learning, extinction and transmission.

A review and agenda for integrated disease models including social and behavioural factors.

Social and behavioural factors are critical to the emergence, spread and containment of human disease, and are key determinants of the course, duration and outcomes of disease outbreaks. Recent epidemics of Ebola in West Africa and coronavirus disease 2019 (COVID-19) globally have reinforced the importance of developing infectious disease models that better integrate social and behavioural dynamics and theories. Meanwhile, the growth in capacity, coordination and prioritization of social science research and of risk communication and community engagement (RCCE) practice within the current pandemic response provides an opportunity for collaboration among epidemiological modellers, social scientists and RCCE practitioners towards a mutually beneficial research and practice agenda. Here, we provide a review of the current modelling methodologies and describe the challenges and opportunities for integrating them with social science research and RCCE practice. Finally, we set out an agenda for advancing transdisciplinary collaboration for integrated disease modelling and for more robust policy and practice for reducing disease transmission.

Data analysis and modeling pipelines for controlled networked social science experiments.

There is large interest in networked social science experiments for understanding human behavior at-scale. Significant effort is required to perform data analytics on experimental outputs and for computational modeling of custom experiments. Moreover, experiments and modeling are often performed in a cycle, enabling iterative experimental refinement and data modeling to uncover interesting insights and to generate/refute hypotheses about social behaviors. The current practice for social analysts is to develop tailor-made computer programs and analytical scripts for experiments and modeling. This often leads to inefficiencies and duplication of effort. In this work, we propose a pipeline framework to take a significant step towards overcoming these challenges. Our contribution is to describe the design and implementation of a software system to automate many of the steps involved in analyzing social science experimental data, building models to capture the behavior of human subjects, and providing data to test hypotheses. The proposed pipeline framework consists of formal models, formal algorithms, and theoretical models as the basis for the design and implementation. We propose a formal data model, such that if an experiment can be described in terms of this model, then our pipeline software can be used to analyze data efficiently. The merits of the proposed pipeline framework is elaborated by several case studies of networked social science experiments.

Longer term follow-up of the randomized phase III trial SWOG S0777: bortezomib, lenalidomide and dexamethasone vs. lenalidomide and dexamethasone in patients (Pts) with previously untreated multiple myeloma without an intent for immediate autologous stem cell transplant (ASCT).

SWOG S0777, a randomized phase III trial, compared bortezomib, lenalidomide and dexamethasone (VRd) with lenalidomide and dexamethasone (Rd). This updated analysis includes 460 patients evaluable for survival endpoints: 225 eligible and analyzable patients were randomized to Rd and 235 to VRd. The 6-month induction was six 28-day cycles of Rd and eight 21-day cycles of VRd followed by Rd maintenance for all patients. Median follow up is 84 months. Median PFS is 41 months for VRd and 29 months for Rd: stratified hazard ratio (96% Wald Confidence Interval) was 0.742 (0.594, 0.928) and one-sided stratified log-rank P-value 0.003. Median OS for VRd is still not reached with median OS for Rd being 69 months: stratified hazard ratio (96% Wald Confidence Interval) was 0.709 (0.543, 0.926) and stratified two-sided P-value was 0.0114. Both PFS and OS were improved with VRd versus Rd adjusting for age (P-values: 0.013 [PFS]; 0.033 [OS])). Median duration of Rd maintenance was 17.1 months. The addition of bortezomib to lenalidomide dexamethasone for induction therapy results in a statistically significant and clinically meaningful improvement in PFS as well as better OS. VRd continues to represent an appropriate standard of care irrespective of age.

The Normative Underpinnings of Population-Level Alcohol Use: An Individual-Level Simulation Model.

Background. By defining what is "normal," appropriate, expected, and unacceptable, social norms shape human behavior. However, the individual-level mechanisms through which social norms impact population-level trends in health-relevant behaviors are not well understood. Aims. To test the ability of social norms mechanisms to predict changes in population-level drinking patterns. Method. An individual-level model was developed to simulate dynamic normative mechanisms and behavioral rules underlying drinking behavior over time. The model encompassed descriptive and injunctive drinking norms and their impact on frequency and quantity of alcohol use. A microsynthesis initialized in 1979 was used as a demographically representative synthetic U.S. population. Three experiments were performed in order to test the modelled normative mechanisms. Results. Overall, the experiments showed limited influence of normative interventions on population-level alcohol use. An increase in the desire to drink led to the most meaningful changes in the population's drinking behavior. The findings of the experiments underline the importance of autonomy, that is, the degree to which an individual is susceptible to normative influence. Conclusion. The model was able to predict theoretically plausible changes in drinking patterns at the population level through the impact of social mechanisms. Future applications of the model could be used to plan norms interventions pertaining to alcohol use as well as other health behaviors.

Lack of Spleen Signal on Diffusion Weighted MRI is associated with High Tumor Burden and Poor Prognosis in Multiple Myeloma: A Link to Extramedullary Hematopoiesis?

Due to the low frequency of abnormalities affecting the spleen, this organ is often overlooked during radiological examinations. Here, we report on the unexpected finding, that the spleen signal on diffusion-weighted MRI (DW-MRI) is associated with clinical parameters in patients with plasma cell dyscrasias. Methods: We investigated the spleen signal on DW-MRI together with clinical and molecular parameters in 295 transplant-eligible newly diagnosed Multiple Myeloma (NDMM) patients and in 72 cases with monoclonal gammopathy of undetermined significance (MGUS). Results: Usually, the spleen is the abdominal organ with the highest intensities on DW-MRI. Yet, significant signal loss on DW-MRI images was seen in 71 of 295 (24%) NDMM patients. This phenomenon was associated with the level of bone marrow plasmacytosis (P=1x10-10) and International Staging System 3 (P=0.0001) but not with gain(1q), and del(17p) or plasma cell gene signatures. The signal was preserved in 72 individuals with monoclonal gammopathy of undetermined significance and generally re-appeared in MM patients responding to treatment, suggesting that lack of signal reflects increased tumor burden. While absence of spleen signal in MM patients with high risk disease defined a subgroup with very poor outcome, re-appearance of the spleen signal after autologous stem cell transplantation was seen in patients with improved outcome. Our preliminary observation suggests that extramedullary hematopoiesis in the spleen is a factor that modifies the DW-MRI signal of this organ. Conclusions: The DW-MRI spleen signal is a promising marker for tumor load and provides prognostic information in MM.

Mesenchymal stem cells gene signature in high-risk myeloma bone marrow linked to suppression of distinct IGFBP2-expressing small adipocytes.

Recent studies suggest that multiple myeloma (MM) induces proliferation and expansion of bone marrow (BM) mesenchymal stem cells (MSCs), but others showed that MM cells induce MSC senescence. To clarify the interaction between MM and MSCs, we exploited our established MSC gene signature to identify gene expression changes in myeloma MSCs and associated functional differences. Single MSCs from patients with MM had changes in expression of genes associated with cellular proliferation and senescence and a higher proportion of senescent cells and lower proliferative potential than those from age-matched healthy donors. Single MSCs from both sources heterogeneously express MSC genes associated with adipogenesis and osteoblastogenesis. We identified the gene encoding insulin-like growth factor-binding protein 2 (IGFBP2), an MSC gene commonly altered in high risk MM, as under-expressed. Morphologically, IGFBP2+ cells are underrepresented in MM BM compared to smouldering MM. Strong IGFBP2 and adiponectin co-expression was detected in a subset of small adipocytes. Co-culturing normal MSCs with myeloma cells suppressed MSC differentiation to adipocytes and osteoblasts, and reduced expression of IGFBP2 and adiponectin. Recombinant IGFBP2 blocked IGF1-mediated myeloma cell growth. Our data demonstrate that myeloma MSCs are less proliferative and that IGFBP2+ small adipocytes are a distinct mesenchymal cell population suppressed by myeloma.

Toward inverse generative social science using multi-objective genetic programming.

Generative mechanism-based models of social systems, such as those represented by agent-based simulations, require that intra-agent equations (or rules) be specified. However there are often many different choices available for specifying these equations, which can still be interpreted as falling within a particular class of mechanisms. Whilst it is important for a generative model to reproduce historically observed dynamics, it is also important for the model to be theoretically enlightening. Genetic programs (our own included) often produce concatenations that are highly predictive but are complex and hard to interpret theoretically. Here, we develop a new method - based on multi-objective genetic programming - for automating the exploration of both objectives simultaneously. We demonstrate the method by evolving the equations for an existing agent-based simulation of alcohol use behaviors based on social norms theory, the initial model structure for which was developed by a team of human modelers. We discover a trade-off between empirical fit and theoretical interpretability that offers insight into the social norms processes that influence the change and stasis in alcohol use behaviors over time.

MAFb protein confers intrinsic resistance to proteasome inhibitors in multiple myeloma.

BACKGROUND: Multiple myeloma (MM) patients with t(14;20) have a poor prognosis and their outcome has not improved following the introduction of bortezomib (Bzb). The mechanism underlying the resistance to proteasome inhibitors (PIs) for this subset of patients is unknown. METHODS: IC50 of Bzb and carfilzomib (CFZ) in human myeloma cell lines (HMCLs) were established by MTT assay. Gene Expression profile (GEP) analysis was used to determine gene expression in primary myeloma cells. Immunoblotting analysis was performed for MAFb and caspase family proteins. Immunofluorescence staining was used to detect the location of MAFb protein in MM cells. Lentiviral infections were used to knock-down MAFb expression in two lines. Apoptosis detection by flow cytometry and western blot analysis was performed to determine the molecular mechanism MAFb confers resistance to proteasome inhibitors. RESULTS: We found high levels of MAFb protein in cell lines with t(14;20), in one line with t(6;20), in one with Igλ insertion into MAFb locus, and in primary plasma cells from MM patients with t(14;20). High MAFb protein levels correlated with higher IC50s of PIs in MM cells. Inhibition of GSK3ß activity or treatment with Bzb or CFZ prevented MAFb protein degradation without affecting the corresponding mRNA level indicating a role for GSK3 and proteasome inhibitors in regulation of MAFb stability. Silencing MAFb restored sensitivity to Bzb and CFZ, and enhanced PIs-induced apoptosis and activation of caspase-3, - 8, - 9, PARP and lamin A/C suggesting that high expression of MAFb protein leads to insensitivity to proteasome inhibitors. CONCLUSION: These results highlight the role of post-translational modification of MAFb in maintaining its protein level, and identify a mechanism by which proteasome inhibitors induced stabilization of MAFb confers resistance to proteasome inhibitors, and provide a rationale for the development of targeted therapeutic strategies for this subset of patients.

The presence of large focal lesions is a strong independent prognostic factor in multiple myeloma.

Spatial intratumor heterogeneity is frequently seen in multiple myeloma (MM) and poses a significant challenge for risk classifiers, which rely on tumor samples from the iliac crest. Because biopsy-based assessment of multiple skeletal sites is difficult, alternative strategies for risk stratification are required. Recently, the size of focal lesions (FLs) was shown to be a surrogate marker for spatial heterogeneity, suggesting that data from medical imaging could be used to improve risk stratification approaches. Here, we investigated the prognostic value of FL size in 404 transplant-eligible, newly diagnosed MM patients. Using diffusion-weighted magnetic resonance imaging with background suppression, we identified the presence of multiple large FLs as a strong prognostic factor. Patients with at least 3 large FLs with a product of the perpendicular diameters >5 cm2 were associated with poor progression-free survival (PFS) and overall survival (OS; median, 2.3 and 3.6 years, respectively). This pattern, seen in 13.8% of patients, was independent of the Revised International Staging System (RISS), gene expression profiling (GEP)-based risk score, gain(1q), or extramedullary disease (hazard ratio, 2.7 and 2.2 for PFS and OS in multivariate analysis, respectively). The number of FLs lost its negative impact on outcome after adjusting for FL size. In conclusion, the presence of at least 3 large FL is a feature of high risk, which can be used to refine the diagnosis of this type of disease behavior and as an entry criterion for risk-stratified trials.

The Pattern of Mesenchymal Stem Cell Expression Is an Independent Marker of Outcome in Multiple Myeloma.

Purpose: Mesenchymal stem cells (MSC) are an essential component of the bone marrow microenvironment and have shown to support cancer evolution in multiple myeloma. Despite the increasing evidence that multiple myeloma MSCs differ from their healthy counterparts, little knowledge exists as to whether MSCs independently influence disease outcome. The aim of this study was to determine the importance of MSCs in disease progression and outcome in multiple myeloma.Experimental Design: To determine the impact of MSCs on multiple myeloma outcome in an in vivo system, we first identified genes from cultured MSCs that were specific to MSC expression and were not or minimally expressed in plasma cells (PC) or other cells present in bone marrow aspirates. We then applied this MSC gene signature to whole bone marrow biopsies of multiple myeloma patients compared with healthy controls and determined MSC expression scores specific to multiple myeloma and predictive of outcome.Results: We show that multiple myeloma MSC gene expression signatures can differentiate multiple myeloma from monoclonal gammopathy and smoldering multiple myeloma (SMM) as well as from healthy controls and treated multiple myeloma patients who have achieved a complete remission. We identified a prognostic gene score based on three MSC specific genes, COL4A1, NPR3 and ITGBL1, that was able to predict progression-free survival in multiple myeloma patients and progression into multiple myeloma from SMM.Conclusions: Our findings show that progression of multiple myeloma and of SMM into multiple myeloma does not rely solely on intrinsic PC factors, but is independently affected by the biology of the surrounding microenvironment. Clin Cancer Res; 24(12); 2913-9. ©2018 AACR.

Clinical characteristics and prognostic factors in multiple myeloma patients with light chain deposition disease.

Light chain deposition disease (LCDD) is characterized by monotypic immunoglobulin depositions which will eventually lead to loss of organ function if left untreated. While the kidney is almost always affected, the presence and degree of LCDD in other organs vary. Ten to thirty percent of LCDD patients have underlying Multiple Myeloma (MM), yet outcome and prognostic markers in this particular patient group are still lacking. Here, we analyzed 69 patients with MM and biopsy proven LCDD and report on renal and extra-renal involvement and its impact on prognosis as well as renal response depending on hematologic response. Coexisting light chain diseases such as AL amyloid and cast nephropathy were found in 30% of patients; those with LCDD and concurrent amyloid tended to have shorter survival. Cardiac involvement by LCDD was seen in one-third of our patients and was associated with shorter overall survival; such patients also had a significantly higher risk of treatment-related mortality (TRM) after stem cell transplant (SCT) compared to LCDD patients without cardiac involvement. This study highlights that MM patients with LCDD present with different clinical features compared to previously reported LCDD cohorts. Rapid initiation of treatment is necessary to prevent progressive renal disease and worse outcome. Coexisting light chain diseases and cardiac involvement are more common than previously reported and confer worse clinical outcome, emphasizing the need for careful patient careful patient evaluation and treatment selection.

Low expression of hexokinase-2 is associated with false-negative FDG-positron emission tomography in multiple myeloma.

18F-Fluorodeoxyglucose (FDG)-positron emission tomography (PET) and diffusion-weighted magnetic resonance imaging with background signal suppression (DWIBS) are 2 powerful functional imaging modalities in the evaluation of malignant plasma cell (PC) disease multiple myeloma (MM). Preliminary observations have suggested that MM patients with extensive disease according to DWIBS may be reported as being disease-free on FDG-PET ("PET false-negative"). The aim of this study was to describe the proportion of PET false-negativity in a representative set of 227 newly diagnosed MM patients with simultaneous assessment of FDG-PET and DWIBS, and to identify tumor-intrinsic features associated with this pattern. We found the incidence of PET false-negativity to be 11%. Neither tumor load-associated parameters, such as degree of bone marrow PC infiltration, nor the PC proliferation rate were associated with this subset. However, the gene coding for hexokinase-2, which catalyzes the first step of glycolysis, was significantly lower expressed in PET false-negative cases (5.3-fold change, P < .001) which provides a mechanistic explanation for this feature. In conclusion, we demonstrate a relevant number of patients with FDG-PET false-negative MM and a strong association between hexokinase-2 expression and this negativity: a finding which may also be relevant for clinical imaging of other hematological cancers.