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628 records · Page 35
All sourcesNASA NTRSDOE OSTI
DOE OSTI2026

Nicotinamide-Loaded Peptoid Nanotubes for Energy Regeneration in Acute Brain Injury

Acute brain injuries such as perinatal asphyxia, stroke, and traumatic brain injury result in ischemia, oxidative stress, excitotoxicity, and inflammation, leading to a depletion of ATP. Nicotinamide adenine dinucleotide (NAD+) is crucial for ATP regeneration and DNA repair during postinjury recovery. However, the therapeutic benefits of NAD+ and its precursors, such as nicotinamide (NAM), are limited by challenges in achieving effective cell-specific intracellular delivery. In this study, we use a nanopeptoid delivery strategy to replenish the cellular redox state and increase energy production in the acutely injured brain. By self-assembling peptoids into tubular structures, we created biocompatible NAM-conjugated peptoid nanotubes (NAM-PNTs) that vary in tubular length. NAM-PNTs demonstrated significant therapeutic benefits by enhancing cell viability and replenishing intracellular ATP levels within 24 h of treatment in oxygen–glucose-deprived (OGD) BV-2 cells. In organotypic brain slices, NAM-PNT treatment promoted glial proliferation, reduced proinflammatory cytokines, and increased anti-inflammatory cytokines after OGD, an ex vivo model of hypoxia-ischemia. The effect of NAM-PNTs is associated with their uptake into microglia via fluid-phase phagocytosis and caveolae-mediated endocytosis. A single systemic dose of NAM-PNTs localized in microglia in the injured hemisphere and reduced brain tissue loss and improved neuropathology after hypoxia-ischemia in term-equivalent rats. These findings highlight the therapeutic potential of NAM-PNTs for cell-specific targeted delivery and energy restoration in the acutely injured neonatal brain. In the neonatal brain injury field, this work demonstrates the development of an innovative nanoparticle platform from first-principles design and synthesis to in vitro screening and then demonstration of efficacy in vivo .

ATP↗
DOE OSTI2023

HD-Bind: Encoding of Molecular Structure with Low Precision, Hyperdimensional Binary Representations

Publicly available collections of drug-like molecules have grown to comprise tens of billions of compounds due to advances in combinatorial chemistry. Traditional methods for identifying "hit" molecules from a large collection of potential drug-like candidates have relied on biophysical theory to compute approximations to the Gibbs free energy of the binding interaction between the drug and its protein target. These approaches have the major drawback that they require exceptional computing capabilities for even relatively small collections of molecules. Hyperdimensional Computing (HDC) is a recently-proposed learning paradigm that represents data with high-dimension binary vectors; this allows the use of low-precision binary vector arithmetic to create models of the data that can be learned without the need for the gradient-based optimization required in many conventional machine learning and deep learning methods. This algorithmic simplicity allows for acceleration in hardware that has been previously demonstrated in a range of application areas. We consider existing HDC approaches for molecular property classification and introduce two novel encodings of a commonly-used molecular representation, the extended connectivity fingerprint (ECFP). We show that HDC-based inference methods are as much as 91 times more efficient than traditional machine learning methods, and achieve an acceleration of nearly nine orders of magnitude compared to molecular docking. Our results show that HDC accelerated methods retain competitive accuracy on a number of well-studied tasks such as molecular property predictions using the MoleculeNet dataset, and bind/no-bind activity classification using the DUD-E and LIT-PCBA datasets. Our work thus motivates further investigation into molecular representation learning to develop ultraefficient pre-screening tools.

Jones, William↗
DOE OSTI2020

Deep learning to generate in silico chemical property libraries and candidate molecules for small molecule identification in complex samples

Comprehensive and unambiguous identification of small molecules in complex samples will revolutionize our understanding of the role of metabolites in biological systems. Existing and emerging technologies have enabled measurement of chemical properties of molecules in complex mixtures and, in concert, are sensitive enough to resolve even stereoisomers. Despite these experimental advances, small molecule identification is inhibited by (i) chemical reference libraries (e.g. mass spectra, collision cross section, and other measurable property libraries) representing <1% of known molecules, limiting the number of possible identifications, and (ii) the lack of a method to generate candidate matches directly from experimental features (i.e. without a library). To this end, we developed a variational autoencoder (VAE) to learn a continuous numerical, or latent, representation of molecular structure to expand reference libraries for small molecule identification. We extended the VAE to include a chemical property decoder, trained as a multitask network, in order to shape the latent representation such that it assembles according to desired chemical properties. The approach is unique in its application to metabolomics and small molecule identification, with its focus on properties that can be obtained from experimental measurements (m/z, CCS) paired with its training paradigm, which involved a cascade of transfer learning iterations. First, molecular representation is learned from a large dataset of structures with m/z labels. Next, in silico property values are used to continue training, as experimental property data is limited. Finally, the network is further refined by being trained with the experimental data. This allows the network to learn as much as possible at each stage, enabling success with progressively smaller datasets without overfitting. Once trained, the network can be used to predict chemical properties directly from structure, as well as generate candidate structures with desired chemical properties. Our approach is orders of magnitude faster than first-principles simulation for CCS property prediction. Additionally, the ability to generate novel molecules along manifolds, defined by chemical property analogues, positions DarkChem as highly useful in a number of application areas, including metabolomics and small molecule identification, drug discovery and design, chemical forensics, and beyond.

Colby, Sean M.↗
DOE OSTI2025

DOME: Directional medical embedding vectors from Electronic Health Records

Motivation: The increasing availability of Electronic Health Record (EHR) systems has created enormous potential for translational research. Recent developments in representation learning techniques have led to effective large-scale representations of EHR concepts along with knowledge graphs that empower downstream EHR studies. However, most existing methods require training with patient-level data, limiting their abilities to expand the training with multi-institutional EHR data. On the other hand, scalable approaches that only require summary-level data do not incorporate temporal dependencies between concepts. Methods: We introduce a DirectiOnal Medical Embedding (DOME) algorithm to encode temporally directional relationships between medical concepts, using summary-level EHR data. Specifically, DOME first aggregates patient-level EHR data into an asymmetric co-occurrence matrix. Then it computes two Positive Pointwise Mutual Information (PPMI) matrices to correspondingly encode the pairwise prior and posterior dependencies between medical concepts. Following that, a joint matrix factorization is performed on the two PPMI matrices, which results in three vectors for each concept: a semantic embedding and two directional context embeddings. They collectively provide a comprehensive depiction of the temporal relationship between EHR concepts. Results: We highlight the advantages and translational potential of DOME through three sets of validation studies. First, DOME consistently improves existing direction-agnostic embedding vectors for disease risk prediction in several diseases, for example achieving a relative gain of 5.5% in the area under the receiver operating characteristic (AUROC) for lung cancer. Second, DOME excels in directional drug-disease relationship inference by successfully differentiating between drug side effects and indications, correspondingly achieving relative AUROC gain over the state-of-the-art methods by 10.8% and 6.6%. Finally, DOME effectively constructs directional knowledge graphs, which distinguish disease risk factors from comorbidities, thereby revealing disease progression trajectories. The source codes are provided at https://github.com/celehs/Directional-EHRembedding.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2020

Structure of an anti-PEG antibody reveals an open ring that captures highly flexible PEG polymers

Polyethylene glycol (PEG) is a polymer routinely used to modify biologics and nanoparticles to prolong blood circulation and reduce immunogenicity of the underlying therapeutic. However, several PEGylated therapeutics induce the development of anti-PEG antibodies (APA), leading to reduced efficacy and increased adverse events. Given the highly flexible structure of PEG, how APA specifically bind PEG remains poorly understood. Here, we report a crystal structure illustrating the structural properties and conformation of the APA 6-3 Fab bound to the backbone of PEG. The structure reveals an open ring-like sub-structure in the Fab paratope, whereby PEG backbone is captured and then stabilized via Van der Waals interactions along the interior and exterior of the ring paratope surface. Our finding illustrates a strategy by which antibodies can bind highly flexible repeated structures that lack fixed conformations, such as polymers. This also substantially advances our understanding of the humoral immune response generated against PEG.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2022

Impact of Surface Chemistry of Ultrasmall Superparamagnetic Iron Oxide Nanoparticles on Protein Corona Formation and Endothelial Cell Uptake, Toxicity, and Barrier Function

Ultrasmall superparamagnetic iron oxide nanoparticles (USPIONs) have been investigated for biomedical applications, including novel contrast agents, magnetic tracers for tumor imaging, targeted drug delivery vehicles, and magneto-mechanical actuators for hyperthermia and thrombolysis. Despite significant progress, recent clinical reports have raised concerns regarding USPION safety related to endothelial cell dysfunction; however, there is limited information on factors contributing to these clinical responses. The influence of USPION surface chemistry on nanoparticle interactions with proteins may impact endothelial cell function leading to adverse responses. Therefore, the goal of this study was to assess the effects of carboxyl-functionalized USPION (CU) or amine-functionalized USPION (AU) (approximately 30 nm diameter) on biological responses in human coronary artery endothelial cells. Increased protein adsorption was observed for AU compared with CU after exposure to serum proteins. Exposure to CU, but not AU, resulted in a concentration-dependent decrease in cell viability and perinuclear accumulation inside cytoplasmic vesicles. Internalization of CU was correlated with endothelial cell functional changes under non-cytotoxic conditions, as evidenced by a marked decreased expression of endothelial-specific adhesion proteins (eg, vascular endothelial-cadherin and platelet endothelial cell adhesion molecule-1) and increased endothelial permeability. Evaluation of downstream signaling indicated endothelial permeability is associated with actin cytoskeleton remodeling, possibly elicited by intracellular events involving reactive oxygen species, calcium ions, and the nanoparticle cellular uptake pathway. This study demonstrated that USPION surface chemistry significantly impacts protein adsorption and endothelial cell uptake, viability, and barrier function. This information will advance the current toxicological profile of USPION and improve development, safety assessment, and clinical outcomes of USPION-enabled medical products.

Toxicology↗
DOE OSTI2021

Fast and Accurate Machine Learning Strategy for Calculating Partial Atomic Charges in Metal–Organic Frameworks

Computational high-throughput screening using molecular simulations is a powerful tool for identifying top-performing metal–organic frameworks (MOFs) for gas storage and separation applications. Accurate partial atomic charges are often required to model the electrostatic interactions between the MOF and the adsorbate, especially when the adsorption involves molecules with dipole or quadrupole moments such as water and CO 2 . Although ab initio methods can be used to calculate accurate partial atomic charges, these methods are impractical for screening large material databases because of the high computational cost. We developed a random forest machine learning model to predict the partial atomic charges in MOFs using a small yet meaningful set of features that represent both the elemental properties and the local environment of each atom. The model was trained and tested on a collection of about 320 000 density-derived electrostatic and chemical (DDEC) atomic charges calculated on a subset of the Computation-Ready Experimental Metal–Organic Framework (CoRE MOF-2019) database and separately on charge model 5 (CM5) charges. The model predicts accurate atomic charges for MOFs at a fraction of the computational cost of periodic density functional theory (DFT) and is found to be transferable to other porous molecular crystals and zeolites. In conclusion, a strong correlation is observed between the partial atomic charge and the average electronegativity difference between the central atom and its bonded neighbors.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2024

Environmental stress mediates groundwater microbial community assembly

Community assembly describes how different ecological processes shape microbial community composition and structure. How environmental factors impact community assembly remains elusive. Here we sampled microbial communities and >200 biogeochemical variables in groundwater at the Oak Ridge Field Research Center, a former nuclear waste disposal site, and developed a theoretical framework to conceptualize the relationships between community assembly processes and environmental stresses. We found that stochastic assembly processes were critical (>60% on average) in shaping community structure, but their relative importance decreased as stress increased. Dispersal limitation and ‘drift’ related to random birth and death had negative correlations with stresses, whereas the selection processes leading to dissimilar communities increased with stresses, primarily related to pH, cobalt and molybdenum. Assembly mechanisms also varied greatly among different phylogenetic groups. As a result, our findings highlight the importance of microbial dispersal limitation and environmental heterogeneity in ecosystem restoration and management.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2022

Complex pH-Dependent Interactions between Weak Polyelectrolyte Block Copolymer Micelles and Molecular Fluorophores

Amphiphilic block copolymers with weak polyelectrolyte blocks can assemble stimulus-responsive nanostructures and interfaces. Applications of these materials in drug delivery, biomimetics, and sensing largely rely on the well-understood swelling of polyelectrolyte chains upon deprotonation, often induced by changes in pH or ionic strength. This deprotonation can also tune interfacial interactions between the polyelectrolyte blocks and surrounding solution, an effect which is less studied than morphological swelling of polyelectrolytes but can be just as critical for intended function. Here, we investigate whether the pH-driven morphological response of polyelectrolyte-bearing nanostructures also affects the interactions of these nanostructures with molecules in solution, using micelles of a short-chain polybutadiene-block-poly(acrylic acid) (pBd–pAA) as a model system. Here we introduce a Förster resonance energy transfer (FRET) approach to probe interactions between micelles and fluorescent molecular solutes as a function of solution pH. As expected, the pAA corona of these pBd–pAA micelles increases in thickness monotonically as a function of pH. However, FRET efficiency, which provides a metric of the spatial proximity of fluorescently labeled micelles and freely diffusing fluorophores, exhibits complex nonmonotonic behavior as a function of pH, indicating that the average separation of micelles and acceptor fluorophores is not strictly correlated with micelle swelling. Dialysis experiments quantify the affinity of fluorophores for micelles as a function of pH, confirming that changes in FRET are driven almost entirely by the pH-dependent affinity of the pAA block for the investigated molecular fluorophores, not simply by a shape change of the pAA corona. This study provides key insights into the interfacial interactions between weak-polyelectrolyte-bearing nanostructures and molecular solutes, of importance for the development of their stimulus-responsive applications.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2022

Cortical Proteins and Individual Differences in Cognitive Resilience in Older Adults

Background and objectives: Cognitive resilience is a well-recognized concept, but knowledge gaps about its underlying mechanisms have made it difficult to develop instruments that identify older adults with high or low resilience. We tested whether aggregating cortical peptides associated with cognitive resilience into an index can identify adults with higher or lower cognitive resilience. Methods: We used data from 1,192 older decedents, including annual clinical testing, indices of 10 Alzheimer disease (AD) and related dementia (ADRD) pathologies, and 226 proteotypic peptides measured in the dorsal lateral prefrontal cortex. We used linear mixed-effects models to identify peptides that were related to cognitive resilience (i.e., cognitive decline not explained by ADRD pathologies [false discovery rate <0.05]). We aggregated the expression levels of these resilience peptides into a person-specific cognitive resilience index and examined its association with AD clinical and pathologic phenotypes. Results: We constructed a resilience index from 52 of 226 peptides related to cognitive resilience. A higher index was associated with slower cognitive decline (estimate 0.05, SE 0.003, p < 0.001) and slower motor decline (estimate 0.005, SE 0.001, p < 0.001). Most resilience peptides (70%) were specific to cognitive decline, but 30% also provided resilience for motor decline. A higher index was also related to a lower burden of AD pathologies (odds ratio [OR] 0.41, SE 0.01, p < 0.001) and modified the association of AD pathology with cognition in that a higher index modified the negative effects of AD pathology on AD dementia proximate to death (OR 0.70, SE 0.14, p = 0.010). Up to 90% of cognitive resilience peptides were related to AD pathologic phenotypes. Discussion: Cortical proteins may provide some degree of cognitive resilience. These multifunctional proteins also seem to provide resilience to other AD clinical phenotypes and have independent associations with ADRD pathologies. Resilience proteins may be high-value therapeutic targets for drug discovery of interventions that maintain brain health in aging adults via multiple pathways.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2022

Mechanism of Peptide Agonist Binding in CXCR4 Chemokine Receptor

Chemokine receptors are key G-protein-coupled receptors (GPCRs) that control cell migration in immune system responses, development of cardiovascular and central nervous systems, and numerous diseases. In particular, the CXCR4 chemokine receptor promotes metastasis, tumor growth and angiogenesis in cancers. CXCR4 is also used as one of the two co-receptors for T-tropic HIV-1 entry into host cells. Therefore, CXCR4 serves as an important therapeutic target for treating cancers and HIV infection. Apart from the CXCL12 endogenous peptide agonist, previous studies suggested that the first 17 amino acids of CXCL12 are sufficient to activate CXCR4. Two 17-residue peptides with positions 1–4 mutated to RSVM and ASLW functioned as super and partial agonists of CXCR4, respectively. However, the mechanism of peptide agonist binding in CXCR4 remains unclear. Here, we have investigated this mechanism through all-atom simulations using a novel Peptide Gaussian accelerated molecular dynamics (Pep-GaMD) method. The Pep-GaMD simulations have allowed us to explore representative binding conformations of each peptide and identify critical low-energy states of CXCR4 activated by the super versus partial peptide agonists. Our simulations have provided important mechanistic insights into peptide agonist binding in CXCR4, which are expected to facilitate rational design of new peptide modulators of CXCR4 and other chemokine receptors.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2025

Antimicrobial Peptides Can Facilitate Whole Blood Safety from Bacteria: A Proof of Concept

With continuous improvements to blood donor deferrals and the availability of sensitive tests for donation screening for infectious agents, bacterial contamination of whole blood (WB) and blood components stored for transfusion is a rare event. Nonetheless, it still occurs and remains a transfusion-associated risk in terms of septic transfusion reactions (STRs) and transfusion-transmitted bacterial infections with morbidity and mortality outcomes. One of the risk mitigation strategies for bacterial contamination is to implement treatment with currently available proactive pathogen reduction technologies (PRTs) for these transfusion products. Here, as a proof of concept, we tested two recently developed unique cationic antimicrobial peptides (AMPs; D-CONGA and D-CONGA-Q7) for WB safety from bacterial contamination. In this study, WB was inoculated with Escherichia coli and Staphylococcus epidermidis and treated with the two peptides to evaluate their bactericidal efficacy. The results demonstrated that D-CONGA and D-CONGA-Q7 exhibit potent inhibitory activity against the bacteria with a minimal inhibitory concentration (MIC) range of 4–8 and 1–8 μM, respectively, depending on the bacterial species tested. Time-kill kinetics further confirmed that the peptides exhibit bactericidal efficacy at 8 μM by achieving a 5-log 10 reduction (99.999%) of the bacterial load in WB with a time-dependent killing profile. Furthermore, even at 20 μM, the AMPs did not negatively impact hemolysis or hemostatic properties. We have further demonstrated using a cationic exchange resin that the cationic AMPs can be separated and removed from WB after the peptide treatments. During 35-day WB storage at 2–8 °C, 4 μM D-CONGA-Q7 one-time treatment prevented S. epidermidis growth and preserved WB quality and integrity. Overall, the results described here provide the first proof of concept that certain AMPs, such as D-CONGA and D-CONGA-Q7, can facilitate WB safety from bacteria during storage.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2024

MIBiG 4.0: advancing biosynthetic gene cluster curation through global collaboration

Specialized or secondary metabolites are small molecules of biological origin, often showing potent biological activities with applications in agriculture, engineering and medicine. Usually, the biosynthesis of these natural products is governed by sets of co-regulated and physically clustered genes known as biosynthetic gene clusters (BGCs). To share information about BGCs in a standardized and machine-readable way, the Minimum Information about a Biosynthetic Gene cluster (MIBiG) data standard and repository was initiated in 2015. Since its conception, MIBiG has been regularly updated to expand data coverage and remain up to date with innovations in natural product research. Here, we describe MIBiG version 4.0, an extensive update to the data repository and the underlying data standard. In a massive community annotation effort, 267 contributors performed 8304 edits, creating 557 new entries and modifying 590 existing entries, resulting in a new total of 3059 curated entries in MIBiG. Particular attention was paid to ensuring high data quality, with automated data validation using a newly developed custom submission portal prototype, paired with a novel peer-reviewing model. MIBiG 4.0 also takes steps towards a rolling release model and a broader involvement of the scientific community. MIBiG 4.0 is accessible online at https://mibig.secondarymetabolites.org/.

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 OSTI2022

Characterizing the Solvation Characteristics of Deep Eutectic Solvents Composed of Active Pharmaceutical Ingredients as a Hydrogen Bond Donor and/or Acceptor

The formation of eutectic mixtures from active pharmaceutical ingredients (APIs) has emerged as an effective technique to develop liquid pharmaceutical formulations. API-based deep eutectic solvents (DESs) enhance the water solubility of APIs and avoid their recrystallization. The physico-chemical properties of API-DESs can be easily modulated by judicious selection of a hydrogen bond acceptor (HBA) or hydrogen bond donor (HBD) excipient that is mixed with the API. Solvation properties of API-DESs can help in understanding the role of ionic and non-ionic excipients towards the overall solvation interactions of these DESs. In this study, the solvation characteristics of three subclasses of API-DESs containing API-HBAs, API-HBDs, or API-HBAs and API-HBDs are investigated. Inverse gas chromatography is employed to study solute-solvent interactions of a variety of probe molecules for twenty different API-DESs. Among the API-DESs comprised of API-HBAs and carboxylic acid or diol-based HBDs, DESs containing diol-based HBDs exhibited stronger dipolar, hydrogen bond basicity, and hydrogen bond acidity interactions compared to carboxylic acid-based HBDs. No significant differences were observed in the solvation properties of DESs comprised of API HBAs and API HBDs. API-DESs comprised of the choline chloride HBA and API-HBDs exhibited stronger hydrogen bond acidity and dipolarity and weaker dispersive-type interactions among the DESs evaluated in this study. An in-depth evaluation of solvation properties for these DESs is important to completely exploit their potential in separation science as well as the fields of biomaterial science, pharmaceutical chemistry, and biotechnology.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2026

The Molecular Structures of Liquid and Glassy Nifedipine and Felodipine and Their Incorporation into PVP

Background: Amorphous drug formulations are commonly used to improve the solubility and bioavailability of poorly soluble molecular pharmaceuticals, yet less is known about their molecular conformations and local bonding interactions than their crystalline phases. Methods: High-energy X-ray diffraction structure factor measurements have been made on liquid and glassy nifedipine (NIF), felodipine (FEL), NIF 1:3 polyvinylpyrrolidone (PVP), and FEL 1:3 PVP wt.% mixtures. The corresponding X-ray pair distribution functions have been interpreted using empirical potential structure refinement using different models and density functional theory conformer calculations. Results: In both NIF and FEL, the NH···O inter-molecular hydrogen bonds between the pyridyl nitrogen and ester carbonyls are found to be considerably weaker than those observed in the crystalline polymorphs. For nifedipine, it is proposed that either inter-molecular NH…ON nitro bonds are present and/or a fraction (<20%) of conformational changes, with the aryl ring flipped, occur in the liquid state. For felodipine, the models indicate significant disorder associated with the methyl and ethyl side chains in the liquid state, with the main peak intensity at 3.0 Å arising from intra-molecular Cl-Cl atom pairs. When nifedipine molecules are incorporated into PVP, our models show they possess stronger NH···O bonds to the PVP polymer than felodipine molecules, which have stronger affinity for bonding to the polymer than to other felodipine molecules. Conclusions: The amorphous forms of both NIF and FEL show much weaker hydrogen bonding than found in their crystalline phases. Liquid NIF also exhibits configurations which are not observed in the crystal phases.

Amorphous Solid Dispersion↗