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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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All sourcesNASA NTRSDOE OSTI
DOE OSTI2021

Recommended reporting items for epidemic forecasting and prediction research: The EPIFORGE 2020 guidelines

Background: The importance of infectious disease epidemic forecasting and prediction research is underscored by decades of communicable disease outbreaks, including COVID-19. Unlike other fields of medical research, such as clinical trials and systematic reviews, no reporting guidelines exist for reporting epidemic forecasting and prediction research despite their utility. We therefore developed the EPIFORGE checklist, a guideline for standardized reporting of epidemic forecasting research. Methods and findings: We developed this checklist using a best-practice process for development of reporting guidelines, involving a Delphi process and broad consultation with an international panel of infectious disease modelers and model end users. The objectives of these guidelines are to improve the consistency, reproducibility, comparability, and quality of epidemic forecasting reporting. The guidelines are not designed to advise scientists on how to perform epidemic forecasting and prediction research, but rather to serve as a standard for reporting critical methodological details of such studies. Conclusions: These guidelines have been submitted to the EQUATOR network, in addition to hosting by other dedicated webpages to facilitate feedback and journal endorsement.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2021

Zinc against COVID-19? Symptom surveillance and deficiency risk groups

A wide variety of symptoms is associated with Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) infection, and these symptoms can overlap with other conditions and diseases. Knowing the distribution of symptoms across diseases and individuals can support clinical actions on timelines shorter than those for drug and vaccine development. Here, we focus on zinc deficiency symptoms, symptom overlap with other conditions, as well as zinc effects on immune health and mechanistic zinc deficiency risk groups. There are well-studied beneficial effects of zinc on the immune system including a decreased susceptibility to and improved clinical outcomes for infectious pathogens including multiple viruses. Zinc is also an anti-inflammatory and anti-oxidative stress agent, relevant to some severe Coronavirus Disease 2019 (COVID-19) symptoms. Unfortunately, zinc deficiency is common worldwide and not exclusive to the developing world. Lifestyle choices and preexisting conditions alone can result in zinc deficiency, and we compile zinc risk groups based on a review of the literature. It is also important to distinguish chronic zinc deficiency from deficiency acquired upon viral infection and immune response and their different supplementation strategies. Zinc is being considered as prophylactic or adjunct therapy for COVID-19, with 12 clinical trials underway, highlighting the relevance of this trace element for global pandemics. Using the example of zinc, we show that there is a critical need for a deeper understanding of essential trace elements in human health, and the resulting deficiency symptoms and their overlap with other conditions. This knowledge will directly support human immune health for decreasing susceptibility, shortening illness duration, and preventing progression to severe cases in the current and future pandemics.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2022

Human behavior determinants of exposure to Anopheles vectors of malaria in Sumba, Indonesia

Malaria vector control interventions in Sumba, Indonesia, have not been able to eliminate malaria. Human drivers of exposure to Anopheles bites were investigated as part of a larger clinical trial evaluating the impact of a spatial repellent product on malaria incidence. Human behavioral observations (HBOs) evaluating temporal and spatial presence, sleeping behaviors, and insecticide treated net (ITN) use, were collected parallel to entomological collections—indoor and outdoor human landing catches (HLCs), and house hold surveys. Data demonstrates that mosquito access to humans, enabled by structurally open houses, is evident by the similar entomological landing rates both inside and outside households. The presence of animals inside houses was associated with increased mosquito entry–however, the number of humans present inside houses was not related to increased mosquito landing. Analyzing mosquito landing rates with human behavior data enables the spatial and temporal estimation of exposure to Anopheles bites, accounting for intervention (ITN) presence and usage. Human behavior adjusted exposure to Anopheles bites was found to be highest in the early in the evening, but continued at lower levels throughout the night. Over the night, most exposure (53%) occurred when people were indoors and not under the protection of nets (asleep or awake) followed by exposure outside (44%). Characterized gaps in protection are outdoor exposure as well as exposure indoors–when awake, and when asleep and not using ITNs. Interestingly, in the primary trial, even though there was not a significant impact of the spatial repellent on vector biting rates by themselves (16%), when factoring in human behavior, there was approximately 28% less exposure in the intervention arm than in the placebo arm. The treated arm had less human behavior adjusted bites in all spaces evaluated though there was proportionally higher exposure indoors. This analysis points to the importance of using HBOs both towards understanding gaps in protection as well as how interventions are evaluated. To mitigate ongoing transmission, understanding context specific spatial and temporal exposure based on the interactions of vectors, humans and interventions would be vital for a directed evidence-based control or elimination strategy.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2023

COVIDScholar: An automated COVID-19 research aggregation and analysis platform

The ongoing COVID-19 pandemic produced far-reaching effects throughout society, and science is no exception. The scale, speed, and breadth of the scientific community’s COVID-19 response lead to the emergence of new research at the remarkable rate of more than 250 papers published per day. This posed a challenge for the scientific community as traditional methods of engagement with the literature were strained by the volume of new research being produced. Meanwhile, the urgency of response lead to an increasingly prominent role for preprint servers and a diffusion of relevant research through many channels simultaneously. These factors created a need for new tools to change the way scientific literature is organized and found by researchers. With this challenge in mind, we present an overview of COVIDScholar https://covidscholar.org/, an automated knowledge portal which utilizes natural language processing (NLP) that was built to meet these urgent needs. The search interface for this corpus of more than 260,000 research articles, patents, and clinical trials served more than 33,000 users at an average of 2,000 monthly active users and a peak of more than 8,600 weekly active users in the summer of 2020. Additionally, we include an analysis of trends in COVID-19 research over the course of the pandemic with a particular focus on the first 10 months, which represents a unique period of rapid worldwide shift in scientific attention.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2020

Radiobiology, Omics and Microdosimetry of Systemic and Targeted Radiotherapeutics Workshop

The Radiobiology, Omics and Microdosimetry of Systemic and Targeted Radiotherapeutics Workshop fostered engagement across disciplines on critical topics that impact the development of targeted radionuclide therapies, bringing together scientific and medical professionals actively involved in radiotherapeutic design, optimization testing, patient treatment, and clinical trials along with basic science researchers, whose novel techniques have the potential to further our understanding of targeted radionuclide therapy. Discussion of the applications of radiotherapeutics to pediatric and adult, brain, prostate and blood cancers, and metastatic disease allowed scientific research to be presented in the context of real-world medical applications and provided an important backdrop to the basic research performed in computers, laboratories, and animal facilities. The workshop format, presentations, and panel discussions were designed to consider a holistic approach to the development of systemic and targeted radiotherapies, integrating radiobiology and microdosimetry, biological assessments on the microscale, and physiologically relevant models of tumor and tissue microenvironments. Important needs highlighted in the workshop include better models to assess the biological effects of radionuclide therapy, novel strategies to target radiotherapeutics to disease sites with high specificity, and new dosimetry techniques that address the importance of tumor microenvironments.

62 RADIOLOGY AND NUCLEAR MEDICINE↗
DOE OSTI2023

Enhancing Radionuclide Production Capabilities at the Duke University Medical Center Cyclotron with a Focus on Astatine-211

Radionuclides play an important role in a diverse array of fields including physics, chemistry, agriculture, materials science, national security, medical research and patient care. Establishing a reliable domestic supply of key radionuclides is critical to our national competitiveness in these areas. Regarding medical applications, targeted alpha-particle therapy (TAT) has become of great interest to basic researchers and clinicians alike and is emerging as a valuable and cost-effective approach for cancer treatment. Unfortunately, progress in TAT, particularly with regard to its clinical translation, has been severely hampered by the limited availability of the most promising radionuclides at a reasonable cost, and with appropriate chemical and radiochemical purity. In this project, we have attempted to address this problem by focusing on production of the 7.2-h half-life α-particle emitter, 211 At, which has long been considered to be one of the most promising radionuclides for TAT. We note that he critical importance of improving the supply of 211 At in the United States was noted in the Funding Opportunity Announcement related to this project. At Duke University, we have a CS-30 cyclotron that is one of the few accelerators in the United States that has an alpha-particle beam that has enough energy to make useful quantities of 211 At. With this cyclotron, we have been able to produce more 211 At than anywhere else in the world because of our unique internal cyclotron target system. However, the CS-30 cyclotron is nearly 40 years old and was no longer reliable. In addition, its operation required considerable skill, largely because it had analog control systems, and vital parts including old-fashioned power supplies, were becoming unavailable. For these reasons, the current project was undertaken to evaluate all the subsystems of the CS-30 cyclotron and replace, repair, and update them. With help from our consultant, Ionetix, this has now been accomplished. We believe that because of this work, the CS-30 can reliably supply 211 At for basic research and clinical trials both at Duke and beyond.

62 RADIOLOGY AND NUCLEAR MEDICINE↗
DOE OSTI2021

IAEA Activities on 67Cu, 186Re, 47Sc Theranostic Radionuclides and Radiopharmaceuticals

Despite interesting properties, the use of 67 Cu, 186 Re and 47 Sc theranostic radionuclides inpreclinical studies and clinical trials is curtailed by their limited availability due to a lack of widelyestablished production methods. An IAEA Coordinated Research Project (CRP) was initiated toidentify important technical issues related to the production and quality control of these emergingradionuclides and related radiopharmaceuticals, based on the request from IAEA Member States.The international team worked on targetry, separation, quality control and radiopharmaceutical aspectsof the radionuclides obtained from research reactors and cyclotrons leading to preparation ofa standard recommendations for all Member States. The CRP was initiated in 2016 with fourteenparticipants from thirteen Member States from four continents. Extraordinary results on the production,quality control and preclinical evaluation of selected radionuclides were reported in this projectthat was finalized in 2020. The outcomes, outputs and results of this project achieved by participatingMember States are described in this minireview.

Pharmacology & Pharmacy↗
DOE OSTI2020

Explainable Artificial Intelligence Recommendation System by Leveraging the Semantics of Adverse Childhood Experiences: Proof-of-Concept Prototype Development

The study of adverse childhood experiences and their consequences has emerged over the past 20 years. Although the conclusions from these studies are available, the same is not true of the data. Accordingly, it is a complex problem to build a training set and develop machine-learning models from these studies. Classic machine learning and artificial intelligence techniques cannot provide a full scientific understanding of the inner workings of the underlying models. This raises credibility issues due to the lack of transparency and generalizability. Explainable artificial intelligence is an emerging approach for promoting credibility, accountability, and trust in mission-critical areas such as medicine by combining machine-learning approaches with explanatory techniques that explicitly show what the decision criteria are and why (or how) they have been established. Hence, thinking about how machine learning could benefit from knowledge graphs that combine “common sense” knowledge as well as semantic reasoning and causality models is a potential solution to this problem. In this study, we aimed to leverage explainable artificial intelligence, and propose a proof-of-concept prototype for a knowledge-driven evidence-based recommendation system to improve mental health surveillance. We used concepts from an ontology that we have developed to build and train a question-answering agent using the Google DialogFlow engine. In addition to the question-answering agent, the initial prototype includes knowledge graph generation and recommendation components that leverage third-party graph technology. To showcase the framework functionalities, we here present a prototype design and demonstrate the main features through four use case scenarios motivated by an initiative currently implemented at a children’s hospital in Memphis, Tennessee. Ongoing development of the prototype requires implementing an optimization algorithm of the recommendations, incorporating a privacy layer through a personal health library, and conducting a clinical trial to assess both usability and usefulness of the implementation. This semantic-driven explainable artificial intelligence prototype can enhance health care practitioners’ ability to provide explanations for the decisions they make.

adverse childhood experiences↗
DOE OSTI2021

An Artificial Intelligence-Assisted Method for Dementia Detection Using Images from the Clock Drawing Test

Background: Widespread dementia detection could increase clinical trial candidates and enable appropriate interventions. Since the Clock Drawing Test (CDT) can be potentially used for diagnosing dementia-related disorders, it can be leveraged to develop a computer-aided screening tool. Objective: To evaluate if a machine learning model that uses images from the CDT can predict mild cognitive impairment or dementia. Methods: Images of an analog clock drawn by 3,263 cognitively intact and 160 impaired subjects were collected during in-person dementia evaluations by the Framingham Heart Study. We processed the CDT images, participant’s age, and education level using a deep learning algorithm to predict dementia status. Results: When only the CDT images were used, the deep learning model predicted dementia status with an area under the receiver operating characteristic curve (AUC) of 81.3% ± 4.3%. A composite logistic regression model using age, level of education, and the predictions from the CDT-only model, yielded an average AUC and average F1 score of 91.9% ±1.1% and 94.6% ±0.4%, respectively. Conclusion: Our modeling framework establishes a proof-of-principle that deep learning can be applied on images derived from the CDT to predict dementia status. When fully validated, this approach can offer a cost-effective and easily deployable mechanism for detecting cognitive impairment.

Neurosciences & Neurology↗
DOE OSTI2022

A Double-Pronged Sword: XJB-5-131 Is a Suppressor of Somatic Instability and Toxicity in Huntington’s Disease

Due to large increases in the elderly populations across the world, age-related diseases are expected to expand dramatically in the coming years. Among these, neurodegenerative diseases will be among the most devastating in terms of their emotional and economic impact on patients, their families, and associated subsidized health costs. There is no currently available cure or rescue for dying brain cells. Viable therapeutics for any of these disorders would be a breakthrough and provide relief for the large number of affected patients and their families. Neurodegeneration is accompanied by elevated oxidative damage and inflammation. While natural antioxidants have largely failed in clinical trials, preclinical phenotyping of the unnatural, mitochondrial targeted nitroxide, XJB-5-131, bodes well for further translational development in advanced animal models or in humans. Here we consider the usefulness of synthetic antioxidants for the treatment of Huntington’s disease. The mitochondrial targeting properties of XJB-5-131 have great promise. It is both an electron scavenger and an antioxidant, reducing both somatic expansion and toxicity simultaneously through the same redox mechanism. By quenching reactive oxygen species, XJB-5-131 breaks the cycle between the rise in oxidative damage during disease progression and the somatic growth of the CAG repeat which depends on oxidation.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2026

Transient histone deacetylase inhibition reveals cell type invariant and specific effects of chromatin decondensation on irradiation response

Radiation therapy plays a prominent role in breast cancer treatment, but the high doses of radiation damage both healthy and cancerous cells. Therefore, additional research is needed into combination therapies that could preferentially radiosensitize cancer cells compared to surrounding healthy tissue without causing deleterious side effects. Histone deacetylase inhibitor drugs (HDACis) have been tested as radiosensitizers in both basic research and clinical trials, but the long exposure time typically used in these treatments and the lack of matched healthy cell controls often leave aspects of their mechanism of action unclear. Here, we show that transient (2 h) trichostatin A (TSA) treatment of cancerous and non-tumorigenic breast epithelial cell lines increases immediate DNA damage and decreases long term cell viability in both cell types at high radiation doses. Transient TSA treatment also causes an increase in DNA damage signals after 5 Gy X-rays in other cancer and healthy cell types: A375 melanoma cells and BJ5-ta fibroblasts. This suggests that chromatin decompaction acts to increase cellular vulnerability to initial DNA damage from high doses of radiation in a cell type independent manner that does not rely on changes to DNA repair pathways caused by longer TSA treatment. However, responses to lower doses of radiation and long term survival are more cell type specific: only MCF7 cells experience an effect of TSA on DNA damage after 1 Gy X-ray radiation while MCF10a cells experience somewhat more evident cell viability effects of combined TSA and radiation treatment long term.

Li, Heng [Biochemistry & Cellular and Molecular Bi↗
DOE OSTI2021

Modeling HIV-1 Within-Host Dynamics After Passive Infusion of the Broadly Neutralizing Antibody VRC01

VRC01 is a broadly neutralizing antibody that targets the CD4 binding site of HIV-1 gp120. Passive administration of VRC01 in humans has assessed the safety and the effect on plasma viremia of this monoclonal antibody (mAb) in a phase 1 clinical trial. After VRC01 infusion, the plasma viral load in most of the participants was reduced but had particular dynamics not observed during antiretroviral therapy. In this paper, we introduce different mathematical models to explain the observed dynamics and fit them to the plasma viral load data. Based on the fitting results we argue that a model containing reversible Ab binding to virions and clearance of virus-VRC01 complexes by a two-step process that includes (1) saturable capture followed by (2) internalization/degradation by phagocytes, best explains the data. This model predicts that VRC01 may enhance the clearance of Ab-virus complexes, explaining the initial viral decay observed immediately after antibody infusion in some participants. Because Ab-virus complexes are assumed to be unable to infect cells, i.e., contain neutralized virus, the model predicts a longer-term viral decay consistent with that observed in the VRC01 treated participants. By assuming a homogeneous viral population sensitive to VRC01, the model provides good fits to all of the participant data. However, the fits are improved by assuming that there were two populations of virus, one more susceptible to antibody-mediated neutralization than the other.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2021

Combinations of Single Chain Variable Fragments From HIV Broadly Neutralizing Antibodies Demonstrate High Potency and Breadth

Broadly neutralizing antibodies (bNAbs) are currently being assessed in clinical trials for their ability to prevent HIV infection. Single chain variable fragments (scFv) of bNAbs have advantages over full antibodies as their smaller size permits improved diffusion into mucosal tissues and facilitates vector-driven gene expression. We have previously shown that scFv of bNAbs individually retain significant breadth and potency. Here we tested combinations of five scFv derived from bNAbs CAP256-VRC26.25 (V2-apex), PGT121 (N332-supersite), 3BNC117 (CD4bs), 8ANC195 (gp120-gp41 interface) and 10E8v4 (MPER). Either two or three scFv were combined in equimolar amounts and tested in the TZM-bl neutralization assay against a multiclade panel of 17 viruses. Experimental IC 50 and IC 80 data were compared to predicted neutralization titers based on single scFv titers using the Loewe additive and the Bliss-Hill model. Like full-sized antibodies, combinations of scFv showed significantly improved potency and breadth compared to single scFv. Combinations of two or three scFv generally followed an independent action model for breadth and potency with no significant synergy or antagonism observed overall although some exceptions were noted. The Loewe model underestimated potency for some dual and triple combinations while the Bliss-Hill model was better at predicting IC 80 titers of triple combinations. Given this, we used the Bliss-Hill model to predict the coverage of scFv against a 45-virus panel at concentrations that correlated with protection in the AMP trials. Using IC 80 titers and concentrations of 1μg/mL, there was 93% coverage for one dual scFv combination (3BNC117+10E8v4), and 96% coverage for two of the triple combinations (CAP256.25+3BNC117+10E8v4 and PGT121+3BNC117+10E8v4). Combinations of scFv, therefore, show significantly improved breadth and potency over individual scFv and given their size advantage, have potential for use in passive immunization.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2024

Structural and functional characterization of IgG- and non-IgG-based T-cell-engaging bispecific antibodies

Bispecific T-cell-engaging antibodies are a growing class of therapeutics with numerous molecules being tested in clinical trials and, currently, seven of them have received market approval. They are structurally complex and function as adaptors to redirect the cytotoxicity of T cells to kill tumor cells. T-cell-engaging bispecific antibodies can be generally divided into two categories: IgG/IgG-like and non-IgG-like formats. Different formats may have different intrinsic potencies and physiochemical properties, and comprehensive studies are needed to gain a better understanding of how the differences in formats impact on structural and functional characteristics. In this study, we designed and generated bispecific T-cell-engaging antibodies with IgG-like (DVD-Ig) and non-IgG (BiTE) formats. Both target the same pair of antigens (EGFR and CD3) to minimize the possible influence of targets on functional characterization. We performed a side-by-side comparison to assess differences in the physiochemical and biological properties of these two bispecific T-cell-engaging antibodies using a variety of breast and ovarian cancer cell-based functional assays to delineate the structural–functional relationships and anti-tumor activities/potency. We found that the Fc portion of T-cell-engaging bispecific antibodies can significantly impact antigen binding activity, potency, and stability in addition to eliciting different mechanisms of action that contribute the killing of cancer cells.

cluster of differentiation 3 (CD3)↗
DOE OSTI2024

A goldilocks computational protocol for inhibitor discovery targeting DNA damage responses including replication-repair functions

While many researchers can design knockdown and knockout methodologies to remove a gene product, this is mainly untrue for new chemical inhibitor designs that empower multifunctional DNA Damage Response (DDR) networks. Here, we present a robust Goldilocks (GL) computational discovery protocol to efficiently innovate inhibitor tools and preclinical drug candidates for cellular and structural biologists without requiring extensive virtual screen (VS) and chemical synthesis expertise. By computationally targeting DDR replication and repair proteins, we exemplify the identification of DDR target sites and compounds to probe cancer biology. Our GL pipeline integrates experimental and predicted structures to efficiently discover leads, allowing early-structure and early-testing (ESET) experiments by many laboratories. By employing an efficient VS protocol to examine protein-protein interfaces (PPIs) and allosteric interactions, we identify ligand binding sites beyond active sites, leveraging in silico advances for molecular docking and modeling to screen PPIs and multiple targets. A diverse 3,174 compound ESET library combines Diamond Light Source DSI-poised, Protein Data Bank fragments, and FDA-approved drugs to span relevant chemotypes and facilitate downstream hit evaluation efficiency for academic laboratories. Two VS per library and multiple ranked ligand binding poses enable target testing for several DDR targets. This GL library and protocol can thus strategically probe multiple DDR network targets and identify readily available compounds for early structural and activity testing to overcome bottlenecks that can limit timely breakthrough drug discoveries. By testing accessible compounds to dissect multi-functional DDRs and suggesting inhibitor mechanisms from initial docking, the GL approach may enable more groups to help accelerate discovery, suggest new sites and compounds for challenging targets including emerging biothreats and advance cancer biology for future precision medicine clinical trials.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2023

A randomised sham-controlled study evaluating rTMS analgesic efficacy for postherpetic neuralgia

Context Postherpetic neuralgia (PHN) is a refractory neuropathic pain condition in which new treatment options are being developed. Repetitive transcranial magnetic stimulation (rTMS) may have the potential to reduce pain sensations in patients with postherpetic neuralgia. Objectives This study investigated the efficacy on postherpetic neuralgia by stimulating two potential targets, the motor cortex (M1) and the dorsolateral prefrontal cortex (DLPFC). Methods This is a double-blind, randomised, sham-controlled study. Potential participants were recruited from Hangzhou First People’s Hospital. Patients were randomly assigned to either the M1, DLPFC or Sham group. Patients received ten daily sessions of 10-Hz rTMS in 2 consecutive weeks. The primary outcome measure was visual analogue scale (VAS) assessed at baseline, first week of treatment (week 1), post-treatment (week 2), 1-week (week 4), 1-month (week 6) and 3-month (week 14) follow-up. Results Of sixty patients enrolled, 51 received treatment and completed all outcome assessments. M1 stimulation resulted in a larger analgesia during and after treatment compared to the Sham (week 2 – week 14, p < 0.005), as well as to the DLPFC stimulation (week 1 – week 14, p < 0.05). In addition to pain, sleep disturbance was significantly improved and relieved by targeting either the M1 or the DLPFC (M1: week 4 – week 14, p < 0.01; DLPFC: week 4 – week 14, p < 0.01). Moreover, pain sensations following M1 stimulation uniquely predicted improvement in sleep quality. Conclusion M1 rTMS is superior to DLPFC stimulation in treating PHN with excellent pain response and long-term analgesia. Meanwhile, M1 and DLPFC stimulation were equally effective in improving sleep quality in PHN. Clinical trial registration https://www.chictr.org.cn/ , identifier ChiCTR2100051963.

Wang, Huan↗
DOE OSTI2021

Characterization of Plasma Protein Alterations in Pregnant and Postpartum Individuals Living With HIV to Support Physiologically-Based Pharmacokinetic Model Development

Alterations in plasma protein concentrations in pregnant and postpartum individuals can influence antiretroviral (ARV) pharmacokinetics. Physiologically-based pharmacokinetic (PBPK) models can serve to inform drug dosing decisions in understudied populations. However, development of such models requires quantitative physiological information (e.g., changes in plasma protein concentration) from the population of interest. Objective: To quantitatively describe the time-course of albumin and α1-acid glycoprotein (AAG) concentrations in pregnant and postpartum women living with HIV. Serum and plasma protein concentrations procured from the International Maternal Pediatric Adolescent AIDS Clinical Trial Protocol 1026s (P1026s) were analyzed using a generalized additive modeling approach. Separate non-parametric smoothing splines were fit to albumin and AAG concentrations as functions of gestational age or postpartum duration. The analysis included 871 and 757 serum albumin concentrations collected from 380 pregnant (~20 to 42 wks gestation) and 354 postpartum (0 to 46 wks postpartum) women, respectively. Thirty-six and 32 plasma AAG concentrations from 31 pregnant (~24 to 38 wks gestation) and 30 postpartum women (~2–13 wks postpartum), respectively, were available for analysis. Estimated mean albumin concentrations remained stable from 20 wks gestation to term (33.4 to 34.3 g/L); whereas, concentrations rapidly increased postpartum until stabilizing at ~42.3 g/L 15 wk after delivery. Estimated AAG concentrations slightly decreased from 24 wks gestation to term (53.6 and 44.9 mg/dL) while postpartum levels were elevated at two wks after delivery (126.1 mg/dL) and subsequently declined thereafter. Computational functions were developed to quantitatively communicate study results in a form that can be readily utilized for PBPK model development. By characterizing the trajectory of plasma protein concentrations in pregnant and postpartum women living with HIV, our analysis can increase confidence in PBPK model predictions for HIV antiretrovirals and better inform drug dosing decisions in this understudied population.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2022

High Accuracy Indicators of Androgen Suppression Therapy Failure for Prostate Cancer—A Modeling Study

Prostate cancer is a serious public health concern in the United States. The primary obstacle to effective long-term management for prostate cancer patients is the eventual development of treatment resistance. Due to the uniquely chaotic nature of the neoplastic genome, it is difficult to determine the evolution of tumor composition over the course of treatment. Hence, a drug is often applied continuously past the point of effectiveness, thereby losing any potential treatment combination with that drug permanently to resistance. If a clinician is aware of the timing of resistance to a particular drug, then they may have a crucial opportunity to adjust the treatment to retain the drug’s usefulness in a potential treatment combination or strategy. In this study, we investigate new methods of predicting treatment failure due to treatment resistance using a novel mechanistic model built on an evolutionary interpretation of Droop cell quota theory. We analyze our proposed methods using patient PSA and androgen data from a clinical trial of intermittent treatment with androgen deprivation therapy. Our results produce two indicators of treatment failure. The first indicator, proposed from the evolutionary nature of the cancer population, is calculated using our mathematical model with a predictive accuracy of 87.3% (sensitivity: 96.1%, specificity: 65%). The second indicator, conjectured from the implication of the first indicator, is calculated directly from serum androgen and PSA data with a predictive accuracy of 88.7% (sensitivity: 90.2%, specificity: 85%). Our results demonstrate the potential and feasibility of using an evolutionary tumor dynamics model in combination with the appropriate data to aid in the adaptive management of prostate cancer.

59 BASIC BIOLOGICAL SCIENCES↗