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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. ispartof: Ieee Journal Of Biomedical And Health Informatics vol:25 issue:3 pages:838-849 ispartof: location:United States status: published

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.

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 the idiosyncratic subject-specific disease profiles. Here, we design a novel longitudinal model to map individual disease trajectories in an automated way using 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.

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.

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.

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. ispartof: Ieee Transactions On Biomedical Engineering vol:67 issue:12 pages:3491-3500 ispartof: location:United States status: published

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.

Objective: Smartphone devices may enable out-of-clinic assessments in chronic neurological diseases. We describe the Draw a Shape (DaS) Test, a smartphone-based and remotely administered test of Upper Extremity (UE) function developed for people with multiple sclerosis (PwMS). This work introduces DaS-related features that characterise UE function and impairment, and aims to demonstrate how multivariate modelling of these metrics can reliably predict the 9-Hole Peg Test (9HPT), a clinician-administered UE assessment in PwMS. Approach: The DaS Test instructed PwMS and healthy controls (HC) to trace predefined shapes on a smartphone screen. A total of 93 subjects (HC, n = 22; PwMS, n = 71) contributed both dominant and non-dominant handed DaS tests. PwMS subjects were characterised as those with normal (nPwMS, n = 50) and abnormal UE function (aPwMS, n = 21) with respect to their average 9HPT time (≤ or > 22.7 (s), respectively). L 1-regularization techniques, combined with linear least squares (OLS, IRLS), or non-linear support vector (SVR) or random forest (RFR) regression were investigated as functions to map relevant DaS features to 9HPT times. Main results: It was observed that average non-dominant handed 9HPT times were more accurately predicted by DaS features (r 2 = 0.41, P < 0.05; MAE: 2.08 ± 0.34 (s)) than average dominant handed 9HPTs (r 2 = 0.39, P < 0.05; MAE: 2.32 ± 0.43 (s)), using simple linear IRLS ( P < 0.01). Moreover, it was found that the Mean absolute error (MAE) in predicted 9HPTs was comparable to the variability of actual 9HPT times within HC, nPwMS and aPwMS groups respectively. The 9HPT however exhibited large heteroscedasticity resulting in less stable predictions of longer 9HPT times. Significance: This study demonstrates the potential of the smartphone-based DaS Test to reliably predict 9HPT times and remotely monitor UE function in PwMS.

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

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. Electronic supplementary material The online version of this article (10.1186/s12938-019-0670-1) contains supplementary material, which is available to authorized users.

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

Background Smartphones, wearables and other consumer technology have high-quality sensors that enable the objective, remote, continuous and longitudinal measurement of disease symptoms. This approach has the potential to be used in future drug development and clinical practice. Aim In this study, we are exploring the feasibility of using smartphone and wrist-worn wearables for high-frequency monitoring of motor and non-motor Huntington’s disease (HD) symptoms. Method Patients with HD in the ongoing ISIS 443139-CS2 clinical study (planned enrollment, n=46) receive a smartphone and wrist-worn wearable during screening. Using these tools, patients are asked to complete daily ‘Active’ tests at home that have been designed to measure a range of motor and non-motor symptoms. Motor symptoms that are being tested include chorea and gait problems; non-motor symptoms that are in focus include cognitive processing speed. In addition, patients are asked to carry the devices with them as they go about their daily activities (i.e., ‘Passive Monitoring’). The sensor data is then securely transferred via WIFI in real-time to Roche, where it is processed and analyzed for adherence and clinically meaningful sensor data features. Results Preliminary data that has been uploaded during the first 8-week observational period in the study show that adherence is good: approximately 85% of subjects completed the entire ‘Active’ test suite on 3 or more days each week. Conclusion It is feasible to measure motor and non-motor function in patients with HD using smartphone-based assessments performed at home. This technology, including real-time data transfer and advanced analytics, provides an innovative way to learn more about patients’ daily symptoms in the clinical trial setting. Data collection in additional patient cohorts will be an important next step to broaden experience with this approach. Funded by Ionis Pharmaceuticals Inc and F. Hoffmann-La Roche.

Smartphone-based assessments have been considered a potential solution for continuously monitoring gait and mobility in mild to moderate Parkinson’s disease (PD) patients. Forty-four PD patients from cohorts 4 to 6 of the Multiple Ascending Dose (MAD) study of PRX002/RG7935 and thirty-five age- and gender-matched healthy individuals (i.e. healthy controls - HC) in a separate study performed smartphone-based assessments for up to 24 weeks and up to 6 weeks, respectively. The assessments included “active gait tests”, where all participants were asked to walk for 30 s with at least one 180\(^\circ \) turn, and “passive monitoring”, in which subjects carried the smartphone in a pocket or fanny pack as part of their daily routine. In total, over 6,600 active gait tests and over 30,000 h of passive monitoring data were collected. A mobility analysis indicates that patients with PD are less mobile than HCs, as manifested in time spent in gait-related activities, number of turns and sit-to-stand transitions, and power per step. It supports the potential use of smartphones for continuous mobility monitoring in future clinical practice and drug development.

Smartphone-based remote monitoring is a potential solution for providing long-term, objective assessment of gait and mobility in patients with Parkinsons disease (PD). In the Multiple Ascending Dose study of PRX002/RG7935, forty-four mild to moderate PD patients from cohorts 4 to 6 were included in a smartphone-based assessment for up to 24 weeks, while in a separate control study, thirty-five age-and gender-matched healthy individuals performed the same assessment up to 6 weeks. In total, over 30,000 hours of sensor data from subjects' daily activities were collected. A convolutional recurrent neural network was used for human activity recognition and extracted gait-related activities, followed by a mobility analysis on extracted mobility features during ambulatory bouts and turns. The analysis revealed that PD patients showed significantly lower mobility in terms of average ambulatory bout length — length of time of one continuous ambulatory segment, average per-step power, turn speed, and number of turns per ambulatory minute. In addition, bout-length stratified analysis shows the between-group difference of multiple features is associated with bout lengths. These study results support the potential use of smartphones for long-term mobility monitoring in future clinical practice, and also shed lights on previously inaccessible relationships between bout length and gait features under free-living condition.

Smartphone-based assessments have been considered a potential solution to passively monitor gait and mobility in early-stage Parkinson's disease (PD) patients. In the Multiple Ascending Dose clinical trial of PRX002/RG7935, 44 PD patients and 35 age- and gender-matched healthy individuals performed smartphone-based assessments for up to 24 weeks and up to 6 weeks respectively. For "passive monitoring", subjects carried the smartphone with them as part of their daily routine, while sensors in the smartphone recording movement data continuously. In total, over 30,000 hours of passive monitoring data were collected. To classify the sensor signal into activity profiles, we built a Human Activity Recognition (HAR) model using Deep Neural Networks (DNN) trained on previously published data. The activity profiles of the participants determined by the HAR model showed significant differences between PD patients and healthy controls in the percentage of time walking and frequency in which subjects changed positions (sitting and standing). This combination of sensor data and machine learning-based activity profiling was shown to hold great promise for use in future clinical practice and drug development.

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.

A combined d. functional theory/continuum electrostatics approach was used to est. the pKa values of metal-based compds. and their hydrolysis products. Specifically, the protonation states and abs. pKa values of [M(h6-arene)(X)(Y)(pta)]n+ (M = Ru or Os; arene = benzene, p-cymene, 1,3,5-trifluorobenzene, benzene-1,3,5-triamine; X, Y = halide, H2O, OH, guanine; pta = 1,3,5-triaza-7-phosphaadamantane) were studied by this approach and supplemented with exptl. pKa detns. using 31P NMR spectroscopy. Compds. of this type were recently used as anticancer agents and also to catalyze CO2 redn. The calcns. show that pta binding to a Ru center significantly reduces the basicity of the ligand. The exptl. obsd. pH-dependent DNA binding is rationalized by the hydroxo/aqua ligand equil. in [Ru(h6-benzene)Cl(OH2)(pta)]+. The applied computational scheme predicts that pKa tuning can be done most effectively by modifications of the arene ligand.

Fast recruitment of the right patients into clinical trials is a key rate determining step for successfulstudy conduct in particular for smaller patient populations in early clinical research. Patientengagement is about establishing a strong partnership between patients' interest and your clinicalresearch program. Today, digital media allow interaction with patients globally and engagement withthem far beyond simple clinical trial participation. We will share key insights from the making ofPioneeringHealthcare.com Roche's new patient-centric approach to patient recruitment &engagement for clinical studies.

In clinical research objective, automated and high frequency measures of diseaseprogression are hard to come by. Traditional physicianledtests are only done periodically, missing the fluctuations of disease activity that strongly affect patient's quality of life. They also lack the objectivity that is so important when developing medicines. By providing patients with mobile sensors they carry day in and day out to conduct tests andcapture the data, we are addressing this need. We designed a custom Android App asking patients to complete six “Active Tests” on adaily basis, wherever they are. After completing the Active Tests, patients will then poptheir phone in their pockets and carry it around with them throughout the day. This is whatwe call “Passive Monitoring”. We have established an end to end approach and technology platform with greatpotential. Our experience will give other clinical studies the confidence that we can bringthe required software and workflows to the clinic. Compliance and ethics hurdles havebeen cleared and investigators trained. We are already gaining experience with patientuptake and are developing the necessary data analysis techniques. Interpretation ofmobile sensor data might soon replace traditional assessments.

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.

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.

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+).

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.

A new series of organometallic ruthenium(II)-arene compounds of the type RuCl2(η6-arene)(phosphine) (phosphine = 1,3,5-triaza-7-phosphaadamantane, PTA, and 3,7-diacetly-1,3,7-triaza-5-phosphabicyclo[3.3.1]nonane, DAPTA) with different potential hydrogen-bonding functionalities on the arene ligand have been prepared and studied for their antitumor activity. Cell viability studies using the TS/A mouse adenocarcinoma cancer cell line and the nontumorigenic HBL-100 human mammary cell line, combined with uptake determinations, are compared to the nonfunctionalized analogues, previously shown to be active on solid metastasizing tumors. The reactivity of the functionalized RAPTA compounds with a 14-mer oligonucleotide (established by mass spectrometry) has been rationalized by DFT calculations, which indicate that environmental factors are important.