Hideaki Nagase, Ph.D.
Kennedy Institute of Rheumatology-Centre for Degenerative Diseases, University of Oxford, UK
Osteoarthritis: diagnosis, treatment and challenges
Hideaki Nagase1, Ngee Han Lim1, George Bou-Gharios1, Ernst Meinjohanns2 and Morten Meldal3
- Kennedy Institute of Rheumatology, Nuffield Department of Orthopaedics, Rheumatology and Musculoskeletal Sciences, University of Oxford, London, W6 8LH UK
- Carlsberg Laboratory, Copenhagen, Denmark,
- Nano-Science Center, Department of Chemistry, University of Copenhagen, Denmark
Osteoarthritis (OA) is the most prevalent age-related degenerative joint disease. With the expanding ageing population, it imposes a major socio-economic burden on society. A key feature of OA is a gradual loss of articular cartilage and deformation of bone, resulting in the impairment of joint function. Currently, there is no effective disease-modifying treatment except joint replacement surgery. There are many possible causes of cartilage loss (e.g. mechanical load, injury, reactive oxygen species, aging, etc.) and etiological factors (obesity, genetics), but the degradation of cartilage is primarily caused by elevated levels of active metalloproteinases. It is therefore attractive to consider proteinase inhibitors as potential therapeutics. However, there are several hurdles to overcome, namely early diagnosis and continuous monitoring of the efficacy of inhibitor therapeutics. We are therefore aiming at developing non-invasive probes to detect cartilage degrading metalloproteinase activities.
We have designed in vivo imaging probes to detect MMP-13 (collagenase 3) activity that participates in OA by degrade cartilage collagen II and MMP-12 (macrophage elastase) activity involved in inflammatory arthritis. These activity-based probes consist of a peptide that is selectively cleaved by the target proteinase, a near-infrared fluorophore and a quencher. The probe’s signal multiplies upon proteolysis. They were first used to follow the respective enzyme activity in vivo in the mouse model of collagen-induced arthritis and we found MMP-12 activity probe (MMP12AP) activation peaked at 5 days after onset of the disease, whereas MMP13AP activation was observed at 10-15 days. The in vivo activation of these probes was inhibited by specific low molecule inhibitors. We proceeded to test both probes in the mouse model of OA induced by the surgical destabilization of medial meniscus of the knee joints. In this model, degradation of knee cartilage is first detected histologically 6 weeks after surgery with significant erosion detectable at 8 weeks. Little activation of MMP12AP was detected, which was expected, as macrophage migration is not obvious in OA. MMP13AP, on the other hand, was significantly activated in the operated knee at 6 weeks compared with the non-operated contralateral knee, but there were no significant differences between the operated and sham-operated knees. At 8 weeks, however, the signals in the operated knees were significantly higher than both the contralateral and sham-operated controls. Activation of aggrecanases and MMP-13 are observed before structural changes of cartilage. We are therefore currently improving the MMP-13 probe for earlier detection by attaching it to polymers that are retained in cartilage.
Terry Hermiston, Ph.D.
Vice President, US Biologics Research Site Head, US Innovation Center Bayer Healthcare, USA
ColoAd1 – An oncolytic adenovirus derived by directed evolution
Attempts at developing oncolytic viruses have been primarily based on rational design. However, this approach has been met with limited success. An alternative approach employs directed evolution as a means of producing highly selective and potent anticancer viruses. In this method, viruses are grown under conditions that enrich and maximize viral diversity and then passaged under conditions meant to mimic those encountered in the human cancer microenvironment. Using the “Directed Evolution” methodology, we have generated ColoAd1, a novel chimeric oncolytic adenovirus. In vitro, this virus demonstrated a >2 log increase in both potency and selectivity when compared to ONYX-015 on colon cancer cells. These results were further supported by in vivo and ex vivo studies. Importantly, these results have validated this methodology as a new general approach for deriving clinically-relevant, highly potent anti-cancer virotherapies. This virus is currently in clinical trials as a novel treatment for cancer.
Srisairam Achuthan, Ph.D.
Senior Scientific Programmer, Research Informatics Division, Department of Information Sciences, City of Hope, CA, USA
Applying Machine learning for Automated Identification of Patient Cohorts
Srisairam Achuthan, Mike Chang, Ajay Shah, Joyce Niland
Patient cohorts for a clinical study are typically identified based on specific selection criteria. In most cases considerable time and effort are spent in finding the most relevant criteria that could potentially lead to a successful study. For complex diseases, this process can be more difficult and error prone since relevant features may not be easily identifiable. Additionally, the information captured in clinical notes is in non-coded text format. Our goal is to discover patterns within the coded and non-coded fields and thereby reveal complex relationships between clinical characteristics across different patients that would be difficult to accomplish manually. Towards this, we have applied machine learning techniques such as artificial neural networks and decision trees to determine patients sharing similar characteristics from available medical records. For this proof of concept study, we used coded and non-coded (i.e., clinical notes) patient data from a clinical database. Coded clinical information such as diagnoses, labs, medications and demographics recorded within the database were pooled together with non-coded information from clinical notes including, smoking status, life style (active / inactive) status derived from clinical notes. The non-coded textual information was identified and interpreted using a Natural Language Processing (NLP) tool I2E from Linguamatics.
H S M Kort, J-W J Lammers, S N W Vorrink, T Troosters
Introduction
Chronic Obstructive Pulmonary Disease (COPD) is a disabling airway disease with variable extrapulmonary effects that may contribute to disease severity in individual patients (Rabe et al. 2007). The world health organization predicts that COPD will become the third leading cause of death worldwide by 2030. Patients with COPD demonstrate reduced levels of spontaneous daily physical activity (DPA) compared with healthy controls (Pitta et al. 2005). This results in a higher risk of hospital admission and shorter survival (Pitta et al. 2006). Pulmonary rehabilitation can help to improve the DPA level, however, obtained benefits decline after 1–2 years (Foglio et al. 2007).
Purpose
In order to maintain DPA in COPD patients after rehabilitation, we developed a mobile phone application. This application measures DPA as steps per day, measured by the accelerometer of the smartphone, and shows the information to the patient via the display of the mobile phone. A physiotherapist can monitor the patient via a secure website where DPA measurements are visible for all patients. Here, DPA goals can be adjusted and text messages sent.
Method
Three pilot studies were performed with healthy students and COPD patients to test the application for usability, user friendliness and reliability with questionnaires and focus groups. Subjects also wore a validated accelerometer. For the Randomized Controlled Trial (RCT) 140 COPD patients will be recruited in Dutch physiotherapy practises. They will be randomised in an intervention group that receives the smartphone for 6 months and a control group. Measurements include lungfunction, dyspnea, and exercise capacity and are held at 0, 3, 6 and 12 months.
Results and Discussion
The application was found to be useful, easy to learn and use. Subjects had no problems with health care professionals seeing information on their physical activity performance. They do find it important to be able to determine who can see the information. Correlations between the accelerometer and the measurements on DPA of the smartphone for steps per hour were 0.69 and 0.70 for pilot studies 1 (students) and 2 (COPD patients) respectively. The version of the application in pilot study 3 contained an error, which made correlations with the accelerometer unusable. The RCT study is now being executed.