Digitalization of toxicology: improving preclinical to clinical translation.

Though the portfolio of medicines that are extending and improving the lives of patients continues to grow, drug discovery and development remains a challenging business on its best day. Safety liabilities are a significant contributor to development attrition where the costliest liabilities to both drug developers and patients emerge in late development or post-marketing. Animal studies are an important and influential contributor to the current drug discovery and development paradigm intending to provide evidence that a novel drug candidate can be used safely and effectively in human volunteers and patients. However, translational gaps-such as toxicity in patients not predicted by animal studies-have prompted efforts to improve their effectiveness, especially in safety assessment. More holistic monitoring and "digitalization" of animal studies has the potential to enrich study outcomes leading to datasets that are more computationally accessible, translationally relevant, replicable, and technically efficient. Continuous monitoring of animal behavior and physiology enables longitudinal assessment of drug effects, detection of effects during the animal's sleep and wake cycles and the opportunity to detect health or welfare events earlier. Automated measures can also mitigate human biases and reduce subjectivity. Reinventing a conservative, standardized, and traditional paradigm like drug safety assessment requires the collaboration and contributions of a broad and multi-disciplinary stakeholder group. In this perspective, we review the current state of the field and discuss opportunities to improve current approaches by more fully leveraging the power of sensor technologies, artificial intelligence (AI), and animal behavior in a home cage environment.

Surface Ligands Dictate the Mechanical Properties of Inorganic Nanomaterials.

The ability for organic surface chemistry to influence the properties of inorganic nanomaterials is appreciated in some instances but is poorly understood in terms of mechanical behavior. Here we demonstrate that the global mechanical strength of a silver nanoplate can be modulated according to the local binding enthalpy of its surface ligands. A continuum-based core-shell model for nanoplate deformation shows that the interior of a particle retains bulk-like properties while the surface shell has yield strength values that depend on surface chemistry. Electron diffraction experiments reveal that, relative to the core, atoms at the nanoplate surface undergo lattice expansion and disordering directly related to the coordinating strength of the surface ligand. As a result, plastic deformation of the shell is more difficult, leading to an enhancement of the global mechanical strength of the plate. These results demonstrate a size-dependent coupling between chemistry and mechanics at the nanoscale.

Discussing sexual activities after total hip arthroplasty.

BACKGROUND: Hip pathology can affect functions including sexual activity. Sex after hip replacement (SAHR) is an important subject for patients but rarely discussed. A conversation prior to surgery can be difficult to initiate, and appropriate advice is then not given. This study has set out to find how physicians approach the subject, and how patients would the like the subject to be addressed. Thus, device a process that ensures appropriate discussions take place prior to Total Hip Replacement (THR) in all patients. METHODS: Questionnaires were given to clinicians and patients. The clinicians' questionnaire asked how they dealt with the issue of SAHR. All patients below the age of 80 were asked how the issue was addressed, and how this could be improved. The aim of this study was twofold. Firstly, to address how SAHR should be approached, both by reviewing the clinicians present views and asking the patients their expectations. Secondly, to develop a process that will ensure patients' concerns are appropriately and consistently addressed prior to total hip replacement (THR). RESULTS: All 17 clinicians responded. None used any printed information to give to patients dealing with SAHR nor did they routinely discuss it with patients. 244/340 patients responded. Over 90% of patients wanted the surgeon to discuss sex after THR with them, and would be happy to be asked directly about the subject. CONCLUSION: Clinicians do not routinely raise the subject of SAHR with patients, who often wanted to know, but rarely asked. There is unease around the subject, and therefore there is a need to establish a process that ensures this discussion takes place prior to THR.

Development of the Digital Arthritis Index, a Novel Metric to Measure Disease Parameters in a Rat Model of Rheumatoid Arthritis.

Despite a broad spectrum of anti-arthritic drugs currently on the market, there is a constant demand to develop improved therapeutic agents. Efficient compound screening and rapid evaluation of treatment efficacy in animal models of rheumatoid arthritis (RA) can accelerate the development of clinical candidates. Compound screening by evaluation of disease phenotypes in animal models facilitates preclinical research by enhancing understanding of human pathophysiology; however, there is still a continuous need to improve methods for evaluating disease. Current clinical assessment methods are challenged by the subjective nature of scoring-based methods, time-consuming longitudinal experiments, and the requirement for better functional readouts with relevance to human disease. To address these needs, we developed a low-touch, digital platform for phenotyping preclinical rodent models of disease. As a proof-of-concept, we utilized the rat collagen-induced arthritis (CIA) model of RA and developed the Digital Arthritis Index (DAI), an objective and automated behavioral metric that does not require human-animal interaction during the measurement and calculation of disease parameters. The DAI detected the development of arthritis similar to standard in vivo methods, including ankle joint measurements and arthritis scores, as well as demonstrated a positive correlation to ankle joint histopathology. The DAI also determined responses to multiple standard-of-care (SOC) treatments and nine repurposed compounds predicted by the SMarTRTM Engine to have varying degrees of impact on RA. The disease profiles generated by the DAI complemented those generated by standard methods. The DAI is a highly reproducible and automated approach that can be used in-conjunction with standard methods for detecting RA disease progression and conducting phenotypic drug screens.

An ingestible sensor for measuring medication adherence.

In this paper, we describe the design and performance of the first integrated-circuit microsensor developed for daily ingestion by patients. The ingestible sensor is a device that allows patients, families, and physicians to measure medication ingestion and adherence patterns in real time, relate pharmaceutical compliance to important physiologic metrics, and take appropriate action in response to a patient's adherence pattern and specific health metrics. The design and theory of operation of the device are presented, along with key in-vitro and in-vivo performance results. The chemical, toxicological, mechanical, and electrical safety tests performed to establish the device's safety profile are described in detail. Finally, aggregate results from multiple clinical trials involving 412 patients and 5656 days of system usage are presented to demonstrate the device's reliability and performance as part of an overall digital health feedback system.

A digital health solution for using and managing medications: wirelessly observed therapy.

Taking oral medication on a prescribed schedule can be a nuisance, especially for elderly individuals and busy people with lots of things on their minds. Nonetheless, taking medication as prescribed is important for maintaining health and well-being. In cases where medication use is part of a clinical trial, taking prescribed medication is important to the entire investigation and outcome of the study, including the determination of whether a drug is effective and safe.

Early clinical experience with networked system for promoting patient self-management.

OBJECTIVE: To gain early experience with a networked system designed to assess a patient's adherence to oral medication and physiologic metrics in an ambulatory, at-home setting. STUDY DESIGN: Prospective, observational studies. MATERIALS AND METHODS: This networked system for patient self-management consists of ingestible markers and a wearable, personal monitor. When a marker is ingested, it communicates to a monitor that time-stamps the ingestion and identifies the marker as unique. The monitor also records heart rate and activity. Data from third-party monitoring equipment (eg, sphygmomanometer, weight scale) can be integrated into the system. Collected data are summarized for patient and physician review. Directly observed ingestion (DOI) of placebo tablet markers was used to assess the system's technical performance. Markers were also coencapsulated with drugs to capture at-home adherence. A performance criterion of <95% was set as the objective for system performance. RESULTS: A total of 111 subjects ingested 7144 ingestible markers; 3298 were DOIs. The system's positive detection accuracy and negative detection accuracy in detecting ingested markers were 97.1% and 97.7%, respectively. It differentiated 100% of multiple drugs and doses taken simultaneously by type and by dose. Medication adherence was >85%. The most common adverse effect was mild skin rash from the monitor's electrodes. No definitive marker-related adverse effects were reported. CONCLUSION: The system appears to be safe and effective in capturing and integrating adherence and physiologic data. Efforts are under way to enhance system functionalities and refine user interfaces. By providing context-rich information, this system may enhance patient-provider collaboration.

A knowledge-based potential function predicts the specificity and relative binding energy of RNA-binding proteins.

RNA-protein interactions are fundamental to gene expression. Thus, the molecular basis for the sequence dependence of protein-RNA recognition has been extensively studied experimentally. However, there have been very few computational studies of this problem, and no sustained attempt has been made towards using computational methods to predict or alter the sequence-specificity of these proteins. In the present study, we provide a distance-dependent statistical potential function derived from our previous work on protein-DNA interactions. This potential function discriminates native structures from decoys, successfully predicts the native sequences recognized by sequence-specific RNA-binding proteins, and recapitulates experimentally determined relative changes in binding energy due to mutations of individual amino acids at protein-RNA interfaces. Thus, this work demonstrates that statistical models allow the quantitative analysis of protein-RNA recognition based on their structure and can be applied to modeling protein-RNA interfaces for prediction and design purposes.

An all-atom, distance-dependent scoring function for the prediction of protein-DNA interactions from structure.

We have developed an all-atom statistical potential function for the prediction of protein-DNA interactions from their structures, and show that this method outperforms similar, lower-resolution statistical potentials in a series of decoy discrimination experiments. The all-atom formalism appears to capture details of atomic interactions that are missed by the lower-resolution methods, with the majority of the discriminatory power arising from its description of short-range atomic contacts. We show that, on average, the method is able to identify 90% of near-native docking decoys within the best-scoring 10% of structures in a given decoy set, and it compares favorably with an optimized physical potential function in a test of structure-based identification of DNA binding-sequences. These results demonstrate that all-atom statistical functions specific to protein-DNA interactions can achieve great discriminatory power despite the limited size of the structural database. They also suggest that the statistical scores may soon be able to achieve accuracy on par with more complex, physical potential functions.

Microholographic multilayer optical disk data storage.

Micrometer-sized reflection holograms can be written into a rapidly rotating homogeneous photopolymer disk at the focus of a high-numerical-aperture beam and its retroreflection to implement high-capacity multilayer digital data storage. This retroreflection is generated by an optical system with positive unity magnification to ensure passive alignment of the counterpropagating beam. Analysis reveals that the storage capacity and transfer rate of this bit-based holographic storage system compare favorably with traditional page-based systems but at a fraction of the system complexity and cost. The analysis is experimentally validated at 532 nm by writing and reading 12 layers of microholograms in a 125-microm photopolymer disk continuously rotating at 3600 rpm. The experimental results predict a capacity limit of 140 Gbytes in a millimeter-thick disk or over 1 Tbyte with the wavelength and numerical aperture of Blu-Ray.

Automated prediction of CASP-5 structures using the Robetta server.

Robetta is a fully automated protein structure prediction server that uses the Rosetta fragment-insertion method. It combines template-based and de novo structure prediction methods in an attempt to produce high quality models that cover every residue of a submitted sequence. The first step in the procedure is the automatic detection of the locations of domains and selection of the appropriate modeling protocol for each domain. For domains matched to a homolog with an experimentally characterized structure by PSI-BLAST or Pcons2, Robetta uses a new alignment method, called K*Sync, to align the query sequence onto the parent structure. It then models the variable regions by allowing them to explore conformational space with fragments in fashion similar to the de novo protocol, but in the context of the template. When no structural homolog is available, domains are modeled with the Rosetta de novo protocol, which allows the full length of the domain to explore conformational space via fragment-insertion, producing a large decoy ensemble from which the final models are selected. The Robetta server produced quite reasonable predictions for targets in the recent CASP-5 and CAFASP-3 experiments, some of which were at the level of the best human predictions.