Use of smartphone-based remote assessments of multiple sclerosis in Floodlight Open, a global, prospective, open-access study.

Floodlight Open was a global, open-access, digital-only study designed to understand the drivers and barriers in deployment and use of a smartphone app in a naturalistic setting and broad study population of people with and without multiple sclerosis (MS). The study utilised the Floodlight Open app: a 'bring-your-own-device' solution that remotely measures a user's mood, cognition, hand motor function, and gait and postural stability via smartphone sensor-based tests requiring active user input ('active tests'). Levels of mobility of study participants ('life-space measurement') were passively measured. Study data from these tests were made available via an open-access platform. Data from 1350 participants with self-declared MS and 1133 participants with self-declared non-MS from 17 countries across four continents were included in this report. Overall, MS participants provided active test data for a mean duration of 5.6 weeks or a mean duration of 19 non-consecutive days. This duration increased among MS participants who persisted beyond the first week to a mean of 10.3 weeks or 36.5 non-consecutive days. Passively collected life-space measurement data were generated by MS participants for a mean duration of 9.8 weeks or 50.6 non-consecutive days. This duration increased to 16.3 weeks/85.1 non-consecutive days among MS participants who persisted beyond the first week. Older age, self-declared MS disease status, and clinical supervision as part of concomitant clinical research were all significantly associated with higher persistence of the use of the Floodlight Open app. MS participants performed significantly worse than non-MS participants on four out of seven active tests. The findings from this multinational study inform future research to improve the dynamics of persistence of use of digital monitoring tools and further highlight challenges and opportunities in applying them to support MS clinical care.

Personalized Longitudinal Assessment of Multiple Sclerosis Using Smartphones.

Personalized longitudinal disease assessment is central to quickly diagnosing, appropriately managing, and optimally adapting the therapeutic strategy of multiple sclerosis (MS). It is also important for identifying idiosyncratic subject-specific disease profiles. Here, we design a novel longitudinal model to map individual disease trajectories in an automated way using smartphone sensor data that may contain missing values. First, we collect digital measurements related to gait and balance, and upper extremity functions using sensor-based assessments administered on a smartphone. Next, we treat missing data via imputation. We then discover potential markers of MS by employing a generalized estimation equation. Subsequently, parameters learned from multiple training datasets are ensembled to form a simple, unified longitudinal predictive model to forecast MS over time in previously unseen people with MS. To mitigate potential underestimation for individuals with severe disease scores, the final model incorporates additional subject-specific fine-tuning using data from the first day. The results show that the proposed model is promising to achieve personalized longitudinal MS assessment; they also suggest that features related to gait and balance as well as upper extremity function, remotely collected from sensor-based assessments, may be useful digital markers for predicting MS over time.

Preliminary validity of the Draw a Shape Test for upper extremity assessment in multiple sclerosis.

OBJECTIVE: To validate the smartphone sensor-based Draw a Shape Test - a part of the Floodlight Proof-of-Concept app for remotely assessing multiple sclerosis-related upper extremity impairment by tracing six different shapes. METHODS: People with multiple sclerosis, classified functionally normal/abnormal via their Nine-Hole Peg Test time, and healthy controls participated in a 24-week, nonrandomized study. Spatial (trace accuracy), temporal (mean and variability in linear, angular, and radial drawing velocities, and dwell time ratio), and spatiotemporal features (trace celerity) were cross-sectionally analyzed for correlation with standard clinical and brain magnetic resonance imaging (normalized brain volume and total lesion volume) disease burden measures, and for capacity to differentiate people with multiple sclerosis from healthy controls. RESULTS: Data from 69 people with multiple sclerosis and 18 healthy controls were analyzed. Trace accuracy (all shapes), linear velocity variability (circle, figure-of-8, spiral shapes), and radial velocity variability (spiral shape) had a mostly fair/moderate-to-good correlation (|r| = 0.14-0.66) with all disease burden measures. Trace celerity also had mostly fair/moderate-to-good correlation (|r| = 0.18-0.41) with Nine-Hole Peg Test performance, cerebellar functional system score, and brain magnetic resonance imaging. Furthermore, partial correlation analysis related these results to motor impairment. People with multiple sclerosis showed greater drawing velocity variability, though slower mean velocity, than healthy controls. Linear velocity (spiral shape) and angular velocity (circle shape) potentially differentiate functionally normal people with multiple sclerosis from healthy controls. INTERPRETATION: The Draw a Shape Test objectively assesses upper extremity impairment and correlates with all disease burden measures, thus aiding multiple sclerosis-related upper extremity impairment characterization.

A Remote Digital Monitoring Platform to Assess Cognitive and Motor Symptoms in Huntington Disease: Cross-sectional Validation Study.

BACKGROUND: Remote monitoring of Huntington disease (HD) signs and symptoms using digital technologies may enhance early clinical diagnosis and tracking of disease progression, guide treatment decisions, and monitor response to disease-modifying agents. Several recent studies in neurodegenerative diseases have demonstrated the feasibility of digital symptom monitoring. OBJECTIVE: The aim of this study was to evaluate a novel smartwatch- and smartphone-based digital monitoring platform to remotely monitor signs and symptoms of HD. METHODS: This analysis aimed to determine the feasibility and reliability of the Roche HD Digital Monitoring Platform over a 4-week period and cross-sectional validity over a 2-week interval. Key criteria assessed were feasibility, evaluated by adherence and quality control failure rates; test-retest reliability; known-groups validity; and convergent validity of sensor-based measures with existing clinical measures. Data from 3 studies were used: the predrug screening phase of an open-label extension study evaluating tominersen (NCT03342053) and 2 untreated cohorts-the HD Natural History Study (NCT03664804) and the Digital-HD study. Across these studies, controls (n=20) and individuals with premanifest (n=20) or manifest (n=179) HD completed 6 motor and 2 cognitive tests at home and in the clinic. RESULTS: Participants in the open-label extension study, the HD Natural History Study, and the Digital-HD study completed 89.95% (1164/1294), 72.01% (2025/2812), and 68.98% (1454/2108) of the active tests, respectively. All sensor-based features showed good to excellent test-retest reliability (intraclass correlation coefficient 0.89-0.98) and generally low quality control failure rates. Good overall convergent validity of sensor-derived features to Unified HD Rating Scale outcomes and good overall known-groups validity among controls, premanifest, and manifest participants were observed. Among participants with manifest HD, the digital cognitive tests demonstrated the strongest correlations with analogous in-clinic tests (Pearson correlation coefficient 0.79-0.90). CONCLUSIONS: These results show the potential of the HD Digital Monitoring Platform to provide reliable, valid, continuous remote monitoring of HD symptoms, facilitating the evaluation of novel treatments and enhanced clinical monitoring and care for individuals with HD.

A smartphone sensor-based digital outcome assessment of multiple sclerosis.

BACKGROUND: Sensor-based monitoring tools fill a critical gap in multiple sclerosis (MS) research and clinical care. OBJECTIVE: The aim of this study is to assess performance characteristics of the Floodlight Proof-of-Concept (PoC) app. METHODS: In a 24-week study (clinicaltrials.gov: NCT02952911), smartphone-based active tests and passive monitoring assessed cognition (electronic Symbol Digit Modalities Test), upper extremity function (Pinching Test, Draw a Shape Test), and gait and balance (Static Balance Test, U-Turn Test, Walk Test, Passive Monitoring). Intraclass correlation coefficients (ICCs) and age- or sex-adjusted Spearman's rank correlation determined test-retest reliability and correlations with clinical and magnetic resonance imaging (MRI) outcome measures, respectively. RESULTS: Seventy-six people with MS (PwMS) and 25 healthy controls were enrolled. In PwMS, ICCs were moderate-to-good (ICC(2,1) = 0.61-0.85) across tests. Correlations with domain-specific standard clinical disability measures were significant for all tests in the cognitive (r = 0.82, p < 0.001), upper extremity function (|r|= 0.40-0.64, all p < 0.001), and gait and balance domains (r = -0.25 to -0.52, all p < 0.05; except for Static Balance Test: r = -0.20, p > 0.05). Most tests also correlated with Expanded Disability Status Scale, 29-item Multiple Sclerosis Impact Scale items or subscales, and/or normalized brain volume. CONCLUSION: The Floodlight PoC app captures reliable and clinically relevant measures of functional impairment in MS, supporting its potential use in clinical research and practice.

Simulating the impact of noise on gait features extracted from smartphone sensor-data for the remote assessment of movement disorders.

Signs and symptoms of movement disorders can be remotely measured at home through sensor-based assessment of gait. However, sensor noise may impact the robustness of such assessments, in particular in a Bring-Your-Own-Device setting where the quality of sensors might vary. Here, we propose a framework to study the impact of inertial measurement unit noise on sensor-based gait features. This framework includes synthesizing realistic acceleration signals from the lower back during a gait cycle in OpenSim, estimating the magnitude of sensor noise from five smartphone models, perturbing the synthesized acceleration signal with the estimated noise in a Monte Carlo simulation, and computing gait features. In addition, we show that realistic levels of sensor noise have only a negligible impact on step power, a measure of gait.

U-turn speed is a valid and reliable smartphone-based measure of multiple sclerosis-related gait and balance impairment.

BACKGROUND: People living with multiple sclerosis (MS) experience impairments in gait and mobility, that are not fully captured with manually timed walking tests or rating scales administered during periodic clinical visits. We have developed a smartphone-based assessment of ambulation performance, the 5 U-Turn Test (5UTT), a quantitative self-administered test of U-turn ability while walking, for people with MS (PwMS). RESEARCH QUESTION: What is the test-retest reliability and concurrent validity of U-turn speed, an unsupervised self-assessment of gait and balance impairment, measured using a body-worn smartphone during the 5UTT? METHODS: 76 PwMS and 25 healthy controls (HCs) participated in a cross-sectional non-randomised interventional feasibility study. The 5UTT was self-administered daily and the median U-turn speed, measured during a 14-day session, was compared against existing validated in-clinic measures of MS-related disability. RESULTS: U-turn speed, measured during a 14-day session from the 5UTT, demonstrated good-to-excellent test-retest reliability in PwMS alone and combined with HCs (intraclass correlation coefficient [ICC] = 0.87 [95 % CI: 0.80-0.92]) and moderate-to-excellent reliability in HCs alone (ICC = 0.88 [95 % CI: 0.69-0.96]). U-turn speed was significantly correlated with in-clinic measures of walking speed, physical fatigue, ambulation impairment, overall MS-related disability and patients' self-perception of quality of life, at baseline, Week 12 and Week 24. The minimal detectable change of the U-turn speed from the 5UTT was low (19.42 %) in PwMS and indicates a good precision of this measurement tool when compared with conventional in-clinic measures of walking performance. SIGNIFICANCE: The frequent self-assessment of turn speed, as an outcome measure from a smartphone-based U-turn test, may represent an ecologically valid digital solution to remotely and reliably monitor gait and balance impairment in a home environment during MS clinical trials and practice.

Smartphone- and Smartwatch-Based Remote Characterisation of Ambulation in Multiple Sclerosis During the Two-Minute Walk Test.

Leveraging consumer technology such as smartphone and smartwatch devices to objectively assess people with multiple sclerosis (PwMS) remotely could capture unique aspects of disease progression. This study explores the feasibility of assessing PwMS and Healthy Control's (HC) physical function by characterising gait-related features, which can be modelled using machine learning (ML) techniques to correctly distinguish subgroups of PwMS from healthy controls. A total of 97 subjects (24 HC subjects, 52 mildly disabled (PwMSmild, EDSS [0-3]) and 21 moderately disabled (PwMSmod, EDSS [3.5-5.5]) contributed data which was recorded from a Two-Minute Walk Test (2MWT) performed out-of-clinic and daily over a 24-week period. Signal-based features relating to movement were extracted from sensors in smartphone and smartwatch devices. A large number of features (n = 156) showed fair-to-strong (R 0.3) correlations with clinical outcomes. LASSO feature selection was applied to select and rank subsets of features used for dichotomous classification between subject groups, which were compared using Logistic Regression (LR), Support Vector Machines (SVM) and Random Forest (RF) models. Classifications of subject types were compared using data obtained from smartphone, smartwatch and the fusion of features from both devices. Models built on smartphone features alone achieved the highest classification performance, indicating that accurate and remote measurement of the ambulatory characteristics of HC and PwMS can be achieved with only one device. It was observed however that smartphone-based performance was affected by inconsistent placement location (running belt versus pocket). Results show that PwMSmod could be distinguished from HC subjects (Acc. 82.2 ± 2.9%, Sen. 80.1 ± 3.9%, Spec. 87.2 ± 4.2%, F 1 84.3 ± 3.8), and PwMSmild (Acc. 82.3 ± 1.9%, Sen. 71.6 ± 4.2%, Spec. 87.0 ± 3.2%, F 1 75.1 ± 2.2) using an SVM classifier with a Radial Basis Function (RBF). PwMSmild were shown to exhibit HC-like behaviour and were thus less distinguishable from HC (Acc. 66.4 ± 4.5%, Sen. 67.5 ± 5.7%, Spec. 60.3 ± 6.7%, F 1 58.6 ± 5.8). Finally, it was observed that subjects in this study demonstrated low intra- and high inter-subject variability which was representative of subject-specific gait characteristics.

Gait Characteristics Harvested During a Smartphone-Based Self-Administered 2-Minute Walk Test in People with Multiple Sclerosis: Test-Retest Reliability and Minimum Detectable Change.

The measurement of gait characteristics during a self-administered 2-minute walk test (2MWT), in persons with multiple sclerosis (PwMS), using a single body-worn device, has the potential to provide high-density longitudinal information on disease progression, beyond what is currently measured in the clinician-administered 2MWT. The purpose of this study is to determine the test-retest reliability, standard error of measurement (SEM) and minimum detectable change (MDC) of features calculated on gait characteristics, harvested during a self-administered 2MWT in a home environment, in 51 PwMS and 11 healthy control (HC) subjects over 24 weeks, using a single waist-worn inertial sensor-based smartphone. Excellent, or good to excellent test-retest reliability were observed in 58 of the 92 temporal, spatial and spatiotemporal gait features in PwMS. However, these were less reliable for HCs. Low SEM% and MDC% values were observed for most of the distribution measures for all gait characteristics for PwMS and HCs. This study demonstrates the inter-session test-retest reliability and provides an indication of clinically important change estimates, for interpreting the outcomes of gait characteristics measured using a body-worn smartphone, during a self-administered 2MWT. This system thus provides a reliable measure of gait characteristics in PwMS, supporting its application for the longitudinal assessment of gait deficits in this population.

Building a Machine-Learning Framework to Remotely Assess Parkinson's Disease Using Smartphones.

OBJECTIVE: Parkinson's disease (PD) is a neurodegenerative disorder that affects multiple neurological systems. Traditional PD assessment is conducted by a physician during infrequent clinic visits. Using smartphones, remote patient monitoring has the potential to obtain objective behavioral data semi-continuously, track disease fluctuations, and avoid rater dependency. METHODS: Smartphones collect sensor data during various active tests and passive monitoring, including balance (postural instability), dexterity (skill in performing tasks using hands), gait (the pattern of walking), tremor (involuntary muscle contraction and relaxation), and voice. Some of the features extracted from smartphone data are potentially associated with specific PD symptoms identified by physicians. To leverage large-scale cross-modality smartphone features, we propose a machine-learning framework for performing automated disease assessment. The framework consists of a two-step feature selection procedure and a generic model based on the elastic-net regularization. RESULTS: Using this framework, we map the PD-specific architecture of behaviors using data obtained from both PD participants and healthy controls (HCs). Utilizing these atlases of features, the framework shows promises to (a) discriminate PD participants from HCs, and (b) estimate the disease severity of individuals with PD. SIGNIFICANCE: Data analysis results from 437 behavioral features obtained from 72 subjects (37 PD and 35 HC) sampled from 17 separate days during a period of up to six months suggest that this framework is potentially useful for the analysis of remotely collected smartphone sensor data in individuals with PD.

Correction: Adherence and Satisfaction of Smartphone- and Smartwatch-Based Remote Active Testing and Passive Monitoring in People With Multiple Sclerosis: Nonrandomized Interventional Feasibility Study.

[This corrects the article DOI: 10.2196/14863.].

Adherence and Satisfaction of Smartphone- and Smartwatch-Based Remote Active Testing and Passive Monitoring in People With Multiple Sclerosis: Nonrandomized Interventional Feasibility Study.

BACKGROUND: Current clinical assessments of people with multiple sclerosis are episodic and may miss critical features of functional fluctuations between visits. OBJECTIVE: The goal of the research was to assess the feasibility of remote active testing and passive monitoring using smartphones and smartwatch technology in people with multiple sclerosis with respect to adherence and satisfaction with the FLOODLIGHT test battery. METHODS: People with multiple sclerosis (aged 20 to 57 years; Expanded Disability Status Scale 0-5.5; n=76) and healthy controls (n=25) performed the FLOODLIGHT test battery, comprising active tests (daily, weekly, every two weeks, or on demand) and passive monitoring (sensor-based gait and mobility) for 24 weeks using a smartphone and smartwatch. The primary analysis assessed adherence (proportion of weeks with at least 3 days of completed testing and 4 hours per day passive monitoring) and questionnaire-based satisfaction. In-clinic assessments (clinical and magnetic resonance imaging) were performed. RESULTS: People with multiple sclerosis showed 70% (16.68/24 weeks) adherence to active tests and 79% (18.89/24 weeks) to passive monitoring; satisfaction score was on average 73.7 out of 100. Neither adherence nor satisfaction was associated with specific population characteristics. Test-battery assessments had an at least acceptable impact on daily activities in over 80% (61/72) of people with multiple sclerosis. CONCLUSIONS: People with multiple sclerosis were engaged and satisfied with the FLOODLIGHT test battery. FLOODLIGHT sensor-based measures may enable continuous assessment of multiple sclerosis disease in clinical trials and real-world settings. TRIAL REGISTRATION: ClinicalTrials.gov: NCT02952911; https://clinicaltrials.gov/ct2/show/NCT02952911.

Look me in the eye: evaluating the accuracy of smartphone-based eye tracking for potential application in autism spectrum disorder research.

BACKGROUND: Avoidance to look others in the eye is a characteristic symptom of Autism Spectrum Disorders (ASD), and it has been hypothesised that quantitative monitoring of gaze patterns could be useful to objectively evaluate treatments. However, tools to measure gaze behaviour on a regular basis at a manageable cost are missing. In this paper, we investigated whether a smartphone-based tool could address this problem. Specifically, we assessed the accuracy with which the phone-based, state-of-the-art eye-tracking algorithm iTracker can distinguish between gaze towards the eyes and the mouth of a face displayed on the smartphone screen. This might allow mobile, longitudinal monitoring of gaze aversion behaviour in ASD patients in the future. RESULTS: We simulated a smartphone application in which subjects were shown an image on the screen and their gaze was analysed using iTracker. We evaluated the accuracy of our set-up across three tasks in a cohort of 17 healthy volunteers. In the first two tasks, subjects were shown different-sized images of a face and asked to alternate their gaze focus between the eyes and the mouth. In the last task, participants were asked to trace out a circle on the screen with their eyes. We confirm that iTracker can recapitulate the true gaze patterns, and capture relative position of gaze correctly, even on a different phone system to what it was trained on. Subject-specific bias can be corrected using an error model informed from the calibration data. We compare two calibration methods and observe that a linear model performs better than a previously proposed support vector regression-based method. CONCLUSIONS: Under controlled conditions it is possible to reliably distinguish between gaze towards the eyes and the mouth with a smartphone-based set-up. However, future research will be required to improve the robustness of the system to roll angle of the phone and distance between the user and the screen to allow deployment in a home setting. We conclude that a smartphone-based gaze-monitoring tool provides promising opportunities for more quantitative monitoring of ASD.

Evaluation of smartphone-based testing to generate exploratory outcome measures in a phase 1 Parkinson's disease clinical trial.

BACKGROUND: Ubiquitous digital technologies such as smartphone sensors promise to fundamentally change biomedical research and treatment monitoring in neurological diseases such as PD, creating a new domain of digital biomarkers. OBJECTIVES: The present study assessed the feasibility, reliability, and validity of smartphone-based digital biomarkers of PD in a clinical trial setting. METHODS: During a 6-month, phase 1b clinical trial with 44 Parkinson participants, and an independent, 45-day study in 35 age-matched healthy controls, participants completed six daily motor active tests (sustained phonation, rest tremor, postural tremor, finger-tapping, balance, and gait), then carried the smartphone during the day (passive monitoring), enabling assessment of, for example, time spent walking and sit-to-stand transitions by gyroscopic and accelerometer data. RESULTS: Adherence was acceptable: Patients completed active testing on average 3.5 of 7 times/week. Sensor-based features showed moderate-to-excellent test-retest reliability (average intraclass correlation coefficient = 0.84). All active and passive features significantly differentiated PD from controls with P < 0.005. All active test features except sustained phonation were significantly related to corresponding International Parkinson and Movement Disorder Society-Sponsored UPRDS clinical severity ratings. On passive monitoring, time spent walking had a significant (P = 0.005) relationship with average postural instability and gait disturbance scores. Of note, for all smartphone active and passive features except postural tremor, the monitoring procedure detected abnormalities even in those Parkinson participants scored as having no signs in the corresponding International Parkinson and Movement Disorder Society-Sponsored UPRDS items at the site visit. CONCLUSIONS: These findings demonstrate the feasibility of smartphone-based digital biomarkers and indicate that smartphone-sensor technologies provide reliable, valid, clinically meaningful, and highly sensitive phenotypic data in Parkinson's disease. © 2018 The Authors. Movement Disorders published by Wiley Periodicals, Inc. on behalf of International Parkinson and Movement Disorder Society.

Patient Recruitment 2.0: Become a Partner in the Patient Journey Using Digital Media.

We describe a digital platform, Pioneering Healthcare, designed to inform and empower people who are impacted by lung cancer. The platform enables Roche to support an online conversation with patients and caregivers about lung cancer, and about the role of lung cancer clinical studies in the development of future treatment options. This conversation is live and ongoing on the platform. It provides insights about the views and motivations of patients, and about how to better support patients pursuing treatment for life-threatening illness. We discuss the strategies used to deploy Pioneering Healthcare, and the advantages of using digital platforms for raising disease awareness, increasing patient engagement and, ultimately, for boosting patient enrollment into clinical trials.

Binding of organometallic ruthenium(II) anticancer compounds to nucleobases: a computational study.

The reaction of the anticancer compound [(eta(6)-benzene)Ru(en)(OH(2))](2+) (1) toward the nucleobases guanine, adenine, and cytosine is studied computationally using DFT/BP86 calculations. The aqua leaving group of such compounds is known to undergo ligand exchange reactions with nucleophilic centers in DNA and preferentially with the N7 atom of guanine, N7(G). Our results show that an H-bonded reactant adduct with nucleobases is formed via either the aqua ligand (cis adduct) or the en (ethylenediamine) ligand (trans adduct) of 1. All studied nucleobases favor an H-bonded cis adduct. Only guanine forms also a trans reactant adduct in the gas phase. The guanine N7 and O6 atoms in this trans adduct are situated in an ideal position to form each a strong H-bond to both amino groups of the en ligand of 1. A docking study shows that this unique recognition pattern is also plausible for the interaction with double stranded DNA. For the reaction of 1 with guanine, we identified three different reaction pathways: (i) A cis (G)N7-Ru-OH(2) transition state (TS). (ii) A direct trans reaction pathway. (iii) A 2-step trans mechanism. The activation energies for the cis pathway are smaller than for the trans pathways. The ultimately formed Ru-N7(G) product is characterized by a thermally stable H-bond between the O6(G) and a diamine-NH(2) hydrogen.

DNA structural distortions induced by ruthenium-arene anticancer compounds.

Organometallic ruthenium(II)-arene (RA) compounds combine a rich structural diversity with the potential to overcome existing chemotherapeutic limitations. In particular, the two classes of compounds [Ru(II)(eta(6)-arene)X(en)] and [Ru(II)(eta(6)-arene)(X)2(pta)] (RA-en and RA-pta, respectively; X = leaving group, en = ethylenediamine, pta = 1,3,5-triaza-7-phosphaadamantane) have become the focus of recent anticancer research. In vitro and in vivo studies have shown that they exhibit promising new activity profiles, for which their interactions with DNA are suspected to be a crucial factor. In the present study, we investigate the binding processes of monofunctional RA-en and bifunctional RA-pta to double-stranded DNA and characterize the resulting structural perturbations by means of ab initio and classical molecular dynamics simulations. We find that both RA complexes bind easily through their ruthenium center to the N7 atom of guanine bases. The high flexibility of DNA allows for fast accommodation of the ruthenium complexes into the major groove. Once bound to the host, however, the two complexes induce different DNA structural distortions. Strain induced in the DNA backbone from RA-en complexation is released by a local break of a Watson-Crick base-pair, consistent with the experimentally observed local denaturation. The bulkier RA-pta, on the other hand, bends the DNA helix toward its major groove, resembling the characteristic DNA distortion induced by the classic anticancer drug cisplatin. The atomistic details of the interactions of RA complexes with DNA gained in the present study shed light on some of the anticancer properties of these compounds and should assist future rational compound design.

NMR solvent shifts of adenine in aqueous solution from hybrid QM/MM molecular dynamics simulations.

We present first principles calculations of the NMR solvent shift of adenine in aqueous solution. The calculations are based on snapshots sampled from a molecular dynamics simulation, which were obtained via a hybrid quantum-mechanical/mechanical modeling approach, using an all-atom force field (TIP3P). We find that the solvation via the strongly fluctuating hydrogen bond network of water leads to nontrivial changes in the NMR spectra of the solutes regarding the ordering of the resonance lines. Although there are still sizable deviations from experiment, the overall agreement is satisfactory for the 1H and 15N NMR shifts. Our work is another step toward a realistic first-principles prediction of NMR chemical shifts in complex chemical environments.

Structural and Energetic Properties of Organometallic Ruthenium(II) Diamine Anticancer Compounds and Their Interaction with Nucleobases.

We rationalize the chemoselectivity of the monofunctional ruthenium anticancer compound [(η(6)-arene)Ru(II)(en)(OH2)](2+) (en=ethylenediamine; arene=benzene 1, p-cymene 2) toward guanine, using static DFT (BP86) and MP2 calculations together with Car-Parrinello molecular dynamics. The calculated binding energies for the three investigated nucleobases (G, A, C) decreases in the order G(N7) ≫ C(O2) ∼ C(N3) > A(N7) > G(O6) > OH2. The G(N7) complex is the most stable product due to a hydrogen bond of its O6 with one of the H2N-amine groups of en, while the corresponding NH2-H2N(en) interaction in the adenine complex is repulsive. A very low rotational barrier of 0.17 kcal/mol (BP86) and 0.64 kcal/mol (MP2) was calculated for the arene rotation in [(η(6)-C6H6)Ru(en)(Cl)](+) (3) allowing complexes containing arenes with bulky side chains like p-cymene to minimize steric interactions with, e.g., DNA by simple arene rotation. All [(η(6)-arene)Ru(en)(L)](2+) compounds exist in two stable conformers obtained for different diamine dihedral angle (NCCN) orientation, which, in the case of asymmetric ligands L, differ by up to ∼2.8 kcal/mol. Car-Parrinello dynamics reveal a chelating transition state for the interconversion between N7 and O6 binding of guanine to [(η(6)-arene)Ru(en)](2+).

Mechanism of bleomycin suicide: a Car-Parrinello molecular dynamics investigation.

Using first-principles molecular dynamics simulations (Car-Parrinello method) we investigated the possible reaction pathways for decay of the active bleomycin-Fe(III)-OOH complex, so-called bleomycin suicide. The theoretical model of activated bleomycin contains the whole metal bonding domain of the bleomycin ligand. Simulations performed both in a vacuum and in water show that a facile decaying process involves a homolytic O-O bond cleavage with an almost simultaneous hydrogen atom abstraction. The formation of an intra- or intermolecular hydrogen bond appears to be crucial for the decay of the activated bleomycin. We did not observe any evidence of heterolytic cleavage of the O-O bond of the Fe(III)-OOH species.