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DOE OSTI2024

Exploration of a Potential DOOR Endpoint for Hospital-acquired Bacterial Pneumonia and Ventilator-associated Bacterial Pneumonia Using Six Registrational Trials for Antibacterial Drugs

Abstract Background Desirability of outcome ranking (DOOR) is an innovative approach to clinical trial design and analysis that uses an ordinal ranking system to incorporate the overall risks and benefits of a therapeutic intervention into a single measurement. Here we derived and evaluated a disease-specific DOOR endpoint for registrational trials for hospital-acquired bacterial pneumonia and ventilator-associated bacterial pneumonia (HABP/VABP). Methods Through comprehensive examination of data from nearly 4000 participants enrolled in six registrational trials for HABP/VABP submitted to the Food and Drug Administration (FDA) between 2005 and 2022, we derived and applied a HABP/VABP specific endpoint. We estimated the probability that a participant assigned to the study treatment arm would have a more favorable overall DOOR or component outcome than a participant assigned to comparator. Results DOOR distributions between treatment arms were similar in all trials. DOOR probability estimates ranged from 48.3% to 52.9% and were not statistically different. There were no significant differences between treatment arms in the component analyses. Although infectious complications and serious adverse events occurred more frequently in ventilated participants compared to non-ventilated participants, the types of events were similar. Conclusions Through a data-driven approach, we constructed and applied a potential DOOR endpoint for HABP/VABP trials. The inclusion of syndrome-specific events may help to better delineate and evaluate participant experiences and outcomes in future HABP/VABP trials and could help inform data collection and trial design.

Immunology↗

DOE OSTI2024

Rapid design of bacteriophage cocktails to suppress the burden and virulence of gut-resident carbapenem-resistant Klebsiella pneumoniae

Antibiotic use can lead to the expansion of multi-drug-resistant pathobionts within the gut microbiome that can cause life-threatening infections. Selective alternatives to conventional antibiotics are in dire need. Here, in this work, we describe a Klebsiella PhageBank for the tailored design of bacteriophage cocktails to treat multi-drug-resistant Klebsiella pneumoniae. Using a transposon library in carbapenem-resistant K. pneumoniae, we identify host factors required for phage infection in major Klebsiella phage families. Leveraging the diversity of the PhageBank, we formulate phage combinations that eliminate K. pneumoniae with minimal phage resistance. Optimized cocktails selectively suppress the burden of K. pneumoniae in the mouse gut and drive the loss of key virulence factors that act as phage receptors. Phage-mediated diversification of bacterial populations in the gut leads to co-evolution of phage variants with higher virulence and broader host range. Altogether, the Klebsiella PhageBank charts a roadmap for phage therapy against a critical multidrug-resistant human pathogen.

60 APPLIED LIFE SCIENCES↗

DOE OSTI2023

Harnessing Aromatic‐Histidine Interactions through Synergistic Backbone Extension and Side Chain Modification

Abstract Peptide engineering efforts have delivered drugs for diverse human diseases. Side chain alteration is among the most common approaches to designing new peptides for specific applications. The peptide backbone can be modified as well, but this strategy has received relatively little attention. Here we show that new and favorable contacts between a His side chain on a target protein and an aromatic side chain on a synthetic peptide ligand can be engineered by rational and coordinated side chain modification and backbone extension. Side chain modification alone was unsuccessful. Binding measurements, high‐resolution structural studies and pharmacological outcomes all support the synergy between backbone and side chain modification in engineered ligands of the parathyroid hormone receptor‐1, which is targeted by osteoporosis drugs. These results should motivate other structure‐based designs featuring coordinated side chain modification and backbone extension to enhance the engagement of peptide ligands with target proteins.

36 MATERIALS SCIENCE↗

DOE OSTI2023

Harnessing Aromatic‐Histidine Interactions through Synergistic Backbone Extension and Side Chain Modification

36 MATERIALS SCIENCE↗

DOE OSTI2023

Deep learning-enabled natural language processing to identify directional pharmacokinetic drug–drug interactions

Background. During drug development, it is essential to gather information about the change of clinical exposure of a drug (object) due to the pharmacokinetic (PK) drug-drug interactions (DDIs) with another drug (precipitant). While many natural language processing (NLP) methods for DDI have been published, most were designed to evaluate if (and what kind of) DDI relationships exist in the text, without identifying the direction of DDI (object vs. precipitant drug). Here we present a method for the automatic identification of the directionality of a PK DDI from literature or drug labels. Methods. We reannotated the Text Analysis Conference (TAC) DDI track 2019 corpus for identifying the direction of a PK DDI and evaluated the performance of a fine-tuned BioBERT model on this task by following the training and validation steps prespecified by TAC. Results. This initial attempt showed the model achieved an F-score of 0.82 in identifying sentences as containing PK DDI and an F-score of 0.97 in identifying object versus precipitant drugs in those sentences. Discussion and conclusion. Despite a growing list of NLP methods for DDI extraction, most of them use a common set of corpora to perform general purpose tasks (e.g., classifying a sentence into one of several fixed DDI categories). There is a lack of coordination between the drug development and biomedical informatics method development community to develop corpora and methods to perform specific tasks (e.g., extract clinical exposure changes due to PK DDI). We hope that our effort can encourage such a coordination so that more “fit for purpose” NLP methods could be developed and used to facilitate the drug development process.

59 BASIC BIOLOGICAL SCIENCES↗

DOE OSTI2023

International Benchmarking: A Global Perspective of Workplace Drug and Alcohol Testing

Workplace drug and alcohol testing has been widely implemented in diverse industries and is designed to detect the use of and deter the influence of legal and illegal substances that would impair the ability to perform work that has significant security and safety implications. Across the globe, patterns of drug and alcohol use and abuse and the regulations controlling drug and alcohol use vary by location (e.g., regions, countries, etc.). The prevalence of workplace drug and alcohol testing and protection of individuals’ rights in the workplace similarly tend to exhibit contextual differences. With the advancements in drug-testing technologies and the recent issuance of guidance for additional testing matrices (e.g., oral fluid, hair, etc.) by the U.S. Department of Health and Human Services, gaining a global perspective on the current state of drug and alcohol testing practices and regulations is important for leveraging such advancements to improve the effectiveness of drug and alcohol testing. The U.S. Nuclear Regulatory Commission (NRC) tasked the Pacific Northwest National Laboratory (PNNL) fitness for duty (FFD) team to conduct a benchmarking analysis of international drug and alcohol testing regulations and programs to identify similarities and differences in workplace testing programs and the basis for any differences. The benchmarking study focuses on a policy analysis and benchmarking metrics comparison among different countries and regions. The policy analysis includes the following three types of relevant international and national policies relevant to drug and alcohol testing in the nuclear industry: 1. National drug and alcohol policy 2. Workplace drug and alcohol testing 3. FFD regulations for Nuclear Power Plants The international drug and alcohol benchmarking metrics focusses on the use of blood, breath, urine, hair, and oral fluid in testing; drug and drug metabolite cutoff values; point-of-collection testing devices; roadside testing methodologies; innovations in drug testing; and the extent of availability of such data. Gaining an understanding of workplace drug and alcohol testing in other countries as well as information about new testing methods, matrices, tools, and technologies that other countries have adopted provides NRC with international comparative information regarding the technical basis for such programs and methods as it relates to the protection of worker and public health and safety.

98 NUCLEAR DISARMAMENT, SAFEGUARDS, AND PHYSICAL P↗

DOE OSTI2023

Building a Computational and Experimental Rapid Response Pipeline to Counter the Coronavirus Disease 2019 Outbreak and Emerging Biothreats

The LDRD ER “Building a Computational and Experimental Rapid Response Pipeline to Counter the Coronavirus Disease 2019 Outbreak and Emerging Biothreats” was conceived to address a need for rapid, scalable, evaluation of computationally designed therapeutic or prophylactic antibodies and vaccine antigens, two important classes of protein medical countermeasure (MCM). This was done in complement to a computationally driven LDRD 20ERD032 “Active Learning for Rapid Design of Vaccines and Antibodies.” Natural antibodies and antigens are often insufficiently broad or robust across different pathogens and their variants. Leveraging a collaboration of simulation driven machine learning, structural expertise, and high-throughput characterization of candidate antibodies, we successfully re-targeted three different anti-SARS-CoV-1 antibodies to neutralize SARS-CoV-2 in vitro. Our antibody design work reached its most important stage in rapid response to the emergence of the Omicron variant of concern (VOC) in late 2021. In a matter of weeks, we computationally designed derivative antibodies of COV2-2130, one of two antibodies from Vanderbilt that form the basis of the AstraZeneca Evusheld prophylactic drug product. This drug product suffers a serious loss of efficacy against Omicron BA.1 and BA.1.1, the first Omicron strains. Our designs were successful, including a pair of designs which provide potent neutralization of not only Omicron BA.1 and BA.1.1, but also the earlier Delta variant, and subsequent Omicron strains including BA.2, BA.4, BA.5, and BA.2.75, demonstrating that our multi-target design process can, by its nature, produce robust antibody designs that strictly improve over the parental antibody. These results, recognized by a 2022 Director’s Science and Technology award, have enabled the follow-on GUIDE program, to commence in FY23.

59 BASIC BIOLOGICAL SCIENCES↗

DOE OSTI2020

Structure-guided design of pure orthosteric inhibitors of αIIbβ3 that prevent thrombosis but preserve hemostasis

A prevailing dogma is that inhibition of vascular thrombosis by antagonizing platelet integrin αIIbβ3 cannot be achieved without compromising hemostasis, thus causing serious bleeding and increased morbidity and mortality. It is speculated that these adverse outcomes result from drug-induced activating conformational changes in αIIbβ3 but direct proof is lacking. Here, we report the structure-guided design of peptide Hr10 and a modified form of the partial agonist drug tirofiban that act as “pure” antagonists of αIIbβ3, i.e., they no longer induce the conformational changes in αIIbβ3. Both agents inhibit human platelet aggregation but preserve clot retraction. Hr10 and modified tirofiban are as effective as partial agonist drugs in inhibiting vascular thrombosis in humanized mice, but neither causes serious bleeding, establishing a causal link between partial agonism and impaired hemostasis. Pure orthosteric inhibitors of αIIbβ3 may thus provide safer alternatives for human therapy, and valuable tools to probe structure–activity relationships in integrins.

60 APPLIED LIFE SCIENCES↗

DOE OSTI2024

Drug providers’ perspectives on antibiotic misuse practices in eastern Ethiopia: a qualitative study

Objective Antibiotic misuse includes using them to treat colds and influenza, obtaining them without a prescription, not finishing the prescribed course and sharing them with others. Although drug providers are well positioned to advise clients on proper stewardship practices, antibiotic misuse continues to rise in Ethiopia. It necessitates an understanding of why drug providers failed to limit such risky behaviours. This study aimed to explore drug providers’ perspectives on antibiotic misuse practices in eastern Ethiopia. Setting The study was conducted in rural Haramaya district and Harar town, eastern Ethiopia. Design and participants An exploratory qualitative study was undertaken between March and June 2023, among the 15 drug providers. In-depth interviews were conducted using pilot-tested, semistructured questions. The interviews were transcribed verbatim, translated into English and analysed thematically. The analyses considered the entire dataset and field notes. Results The study identified self-medication pressures, non-prescribed dispensing motives, insufficient regulatory functions and a lack of specific antibiotic use policy as the key contributors to antibiotic misuse. We found previous usage experience, a desire to avoid extra costs and a lack of essential diagnostics and antibiotics in public institutions as the key drivers of non-prescribed antibiotic access from private drug suppliers. Non-prescribed antibiotic dispensing in pharmacies was driven by client satisfaction, financial gain, business survival and market competition from informal sellers. Antibiotic misuse in the setting has also been linked to traditional and ineffective dispensing audits, inadequate regulatory oversights and policy gaps. Conclusion This study highlights profits and oversimplified access to antibiotics as the main motivations for their misuse. It also identifies the traditional antibiotic dispensing audit as an inefficient regulatory operation. Hence, enforcing specific antibiotic usage policy guidance that entails an automated practice audit, a responsible office and insurance coverage for persons with financial limitations can help optimise antibiotic use while reducing resistance consequences.

General & Internal Medicine↗

DOE OSTI2025

Assessing the potential of deep learning for protein–ligand docking

The effects of ligand binding on protein structures and their in vivo functions carry numerous implications for modern biomedical research and biotechnology development efforts such as drug discovery. Although several deep learning (DL) methods and benchmarks designed for protein–ligand docking have recently been introduced, so far no previous works have systematically studied the behaviour of the latest docking and structure prediction methods within the broadly applicable context of: (1) using predicted (apo) protein structures for docking (for example, for applicability to new proteins); (2) binding multiple (cofactor) ligands concurrently to a given target protein (for example, for enzyme design); and (3) having no previous knowledge of binding pockets (for example, for generalization to unknown pockets). To enable a deeper understanding of the real-world utility of docking methods, we introduce PoseBench, a comprehensive benchmark for broadly applicable protein–ligand docking. PoseBench enables researchers to rigorously and systematically evaluate DL methods for apo-to-holo protein–ligand docking and protein–ligand structure prediction using both primary ligand and multiligand benchmark datasets, the latter of which we introduce to the DL community. Empirically, using PoseBench, we find that: (1) DL cofolding methods generally outperform comparable conventional and DL docking baseline algorithms, but popular methods such as AlphaFold 3 are still challenged by prediction targets with new protein–ligand binding poses; (2) certain DL cofolding methods are highly sensitive to their input multiple sequence alignments, whereas others are not; and (3) DL methods struggle to strike a balance between structural accuracy and chemical specificity when predicting new or multiligand protein targets.

Morehead, Alex [Lawrence Berkeley National Laborat↗

NASA NTRS1990

Parallel line analysis: multifunctional software for the biomedical sciences

An easy to use, interactive FORTRAN program for analyzing the results of parallel line assays is described. The program is menu driven and consists of five major components: data entry, data editing, manual analysis, manual plotting, and automatic analysis and plotting. Data can be entered from the terminal or from previously created data files. The data editing portion of the program is used to inspect and modify data and to statistically identify outliers. The manual analysis component is used to test the assumptions necessary for parallel line assays using analysis of covariance techniques and to determine potency ratios with confidence limits. The manual plotting component provides a graphic display of the data on the terminal screen or on a standard line printer. The automatic portion runs through multiple analyses without operator input. Data may be saved in a special file to expedite input at a future time.

NASA Discipline Cell Biology↗

DOE OSTI2021

AI-based language models powering drug discovery and development

The discovery and development of new medicines is expensive, time-consuming, and often inefficient, with many failures along the way. Powered by artificial intelligence (AI), language models (LMs) have changed the landscape of natural language processing (NLP), offering possibilities to transform treatment development more effectively. Here, we summarize advances in AI-powered LMs and their potential to aid drug discovery and development. We highlight opportunities for AI-powered LMs in target identification, clinical design, regulatory decision-making, and pharmacovigilance. We specifically emphasize the potential role of AI-powered LMs for developing new treatments for Coronavirus 2019 (COVID-19) strategies, including drug repurposing, which can be extrapolated to other infectious diseases that have the potential to cause pandemics. Finally, we set out the remaining challenges and propose possible solutions for improvement.

60 APPLIED LIFE SCIENCES↗

DOE OSTI2022

The influence of molecular design on structure–property relationships of a supramolecular polymer prodrug

Supramolecular self-assemblies of hydrophilic macromolecules functionalized with hydrophobic, structure-directing components have long been used for drug delivery. In these systems, loading of poorly soluble compounds is typically achieved through physical encapsulation during or after formation of the supramolecular assembly, resulting in low encapsulation efficiencies and limited control over release kinetics, which are predominately governed by diffusion and carrier degradation. To overcome these limitations, amphiphilic prodrugs that leverage a hydrophobic drug as both the therapeutic and structure-directing component can be used to create supramolecular materials with higher loading and controlled-release kinetics using biodegradable or enzymatically cleavable linkers. Here, we report the design, synthesis, and characterization of a library of supramolecular polymer prodrugs based on poly(ethylene glycol) (PEG) and the proregenerative drug 1,4-dihydrophenonthrolin-4-one-3-carboxylic acid (DPCA). Structure–property relationships were elucidated through experimental characterization of prodrug behavior in both the wet and dry states using scattering techniques and electron microscopy and corroborated by coarse-grained modeling. Molecular architecture and the hydrophobic-to-hydrophilic ratio of PEG–DPCA conjugates strongly influenced their physical state in water, ranging from fully soluble to supramolecular spherical assemblies and nanofibers. Molecular design and supramolecular structure, in turn, were shown to dramatically alter hydrolytic and enzymatic release and cellular transport of DPCA. In addition to potentially expanding therapeutic options for DPCA through control of supramolecular assemblies, the design principles elaborated here may inform the development of other supramolecular prodrugs based on hydrophobic small-molecule compounds.

77 NANOSCIENCE AND NANOTECHNOLOGY↗

DOE OSTI2023

Fragment-based drug nanoaggregation reveals drivers of self-assembly

Drug nanoaggregates are particles that can deleteriously cause false positive results during drug screening efforts, but alternatively, they may be used to improve pharmacokinetics when developed for drug delivery purposes. The structural features of molecules that drive nanoaggregate formation remain elusive, however, and the prediction of intracellular aggregation and rational design of nanoaggregate-based carriers are still challenging. We investigate nanoaggregate self-assembly mechanisms using small molecule fragments to identify the critical molecular forces that contribute to self-assembly. We find that aromatic groups and hydrogen bond acceptors/donors are essential for nanoaggregate formation, suggesting that both π-π stacking and hydrogen bonding are drivers of nanoaggregation. We apply structure-assembly-relationship analysis to the drug sorafenib and discover that nanoaggregate formation can be predicted entirely using drug fragment substructures. We also find that drug nanoaggregates are stabilized in an amorphous core-shell structure. These findings demonstrate that rational design can address intracellular aggregation and pharmacologic/delivery challenges in conventional and fragment-based drug development processes.

77 NANOSCIENCE AND NANOTECHNOLOGY↗

DOE OSTI2026

Quantitative SANS and multi-model analysis of spacer-dependent micellization of urea-based gemini surfactants

The micellization behavior of urea-based cationic gemini surfactants was investigated using small-angle neutron scattering (SANS) with multi-model form factor analysis. A homologous series of surfactants with urea group included in the hydrophobic tail and polymethylene spacers consisting of two to ten methylene units was analyzed using three form factor models: a core–shell ellipsoid and two variants of homogeneous ellipsoids. The results from all models show a consistent trend of the micelle structures, confirming that the spacer length critically influences micellar geometry, aggregation number, and hydration. The surfactant with four CH 2 groups in the spacer formed the largest micelles with the highest aggregation number, while longer spacers led to progressively smaller, more compact aggregates. The shell hydration—quantified as the volume fraction of heavy water within the hydrophilic region—decreased systematically with increasing spacer length due to enhanced hydrophobicity of the headgroup-spacer region. Intermicellar interactions, modeled as screened Coulomb interaction using the rescaled mean spherical approximation (RMSA), revealed the strongest electrostatic repulsion for the case of four methylene groups in the spacer, corresponding to the highest micellar charge and largest interparticle spacing. The observed spacer-dependent trends were robust across all modeling approaches, demonstrating that the spacer length serves as a key structural determinant of self-assembly in this type of urea-based gemini systems. These findings provide insight into the design of gemini surfactants with tailored aggregation behavior for applications in drug delivery, nanostructure templating, and solubilization technologies.

Core–shell ellipsoid model↗

DOE OSTI2023

Room-temperature structural studies of SARS-CoV-2 protein NendoU with an X-ray free-electron laser

NendoU from SARS-CoV-2 is responsible for the virus’s ability to evade the innate immune system by cleaving the polyuridine leader sequence of antisense viral RNA. Here we report the room-temperature structure of NendoU, solved by serial femtosecond crystallography at an X-ray free-electron laser to 2.6 Å resolution. The room-temperature structure provides insight into the flexibility, dynamics, and other intrinsic properties of NendoU, with indications that the enzyme functions as an allosteric switch. Functional studies examining cleavage specificity in solution and in crystals support the uridine-purine cleavage preference, and we demonstrate that enzyme activity is fully maintained in crystal form. Optimizing the purification of NendoU and identifying suitable crystallization conditions set the benchmark for future time-resolved serial femtosecond crystallography studies. This could advance the design of antivirals with higher efficacy in treating coronaviral infections, since drugs that block allosteric conformational changes are less prone to drug resistance.

59 BASIC BIOLOGICAL SCIENCES↗

DOE OSTI2021

Current status and future outlook for patient‐derived cancer models from a rare cancer research perspective

Abstract Rare cancers are a group of approximately 200 malignancies with extremely low incidences and with a wide variety of genotypes and phenotypes. Collectively, they are more common than any single malignancy. However, given the small numbers of individuals diagnosed with rare cancers, it is difficult to design clinical trials with sufficient patient numbers. Therefore, few effective anticancer drugs have been developed, and evidence‐based medicine is not always feasible for rare cancers. Consequently, their clinical outcomes are generally poorer. Cancer research requires adequate models that faithfully recapitulate molecular features and reproduce treatment responses of the original tumors. Such models allow us to focus on more efficacious drugs in the clinical studies. For rare cancers, patient‐derived cancer models are particularly important because the enrollment of sufficient patients is rarely attainable within a reasonable period of time. However, extremely few models are available for rare cancers. For example, cell lines and xenografts are available for only a limited number of histological subtypes of sarcomas; therefore, most sarcoma research is performed without such models, and a lack of adequate cancer models causes a lag in therapeutic development. The establishment of novel rare cancer models will dramatically facilitate rare cancer research and treatment development in the near future. This review focuses on the status of patient‐derived rare cancer models and discusses their pivotal problems and possibilities, using sarcomas as a representative rare cancer type. Multi‐institutional collaboration will help address the scarcity of patient‐derived rare cancer models.

Kondo, Tadashi↗

DOE OSTI2021

Optimizing FPGA-based Accelerator Design for Large-Scale Molecular Similarity Search (Special Session Paper)

Molecular similarity search has been widely used in drug discovery to rapidly identify structurally similar compounds from large molecular databases. With the increasing size of chemical libraries, there is growing interest in the efficient ac- celeration of large-scale similarity search. Existing works mainly focus on CPU and GPU to accelerate the computation of Tatimoto coefficient in measuring the pairwise similarity between different molecular fingerprints. In this paper, we propose and optimize an FPGA-based accelerator design on exhaustive and approximate search algorithms. On exhaustive search using BitBound & fold- ing, we analyze the similarity cutoff and folding level relationship with search speedup and accuracy, and propose a scalable on- the-fly query engine on FPGAs to reduce the resource utilization and pipeline interval. We achieve a 450 million compounds-per- second processing throughput for a single query engine. On approximate search using hierarchical navigable small world (HNSW), a popular algorithm with high recall and query speed, we propose an FPGA-based graph traversal engine to utilize high throughput register array based priority queue and fine- grained distance calculation engine to increase the processing capability. Experimental results show that the proposed FPGA- based HNSW implementation achieves a 35× speedup than existing works on CPU. To the best of our knowledge, our FPGA- based implementation is the first attempt to accelerate molecular similarity search on FPGA and has the highest performance among existing approaches.

Peng, Hongwu↗