Drug Development in Osteoarthritis
Despite millions of sufferers around the world and the market valued at over $3.5 billion a year, osteoarthritis treatment still largely relies on cheap generic drugs. Only a few companies have disease-modifying biologic drugs at the late stage of development. Among them: Tissuegene, Nordic Biosciences / Merck and Seikagaku. Several others are developing drugs for pain reduction and many many have failed so far. To date, the total investment in failed osteoarthritis (OA) drugs is measured in billions.
An overview of ‘Emerging Drugs for Osteoarthritis’, June 2016, lists several potential issues with development of novel drugs for OA:
- Patient Phenotyping. Because the precise cause of OA is unknown, drug target selection is extremely challenging. It has become clearer that drug candidates should be developed with better understanding of the variability in disease manifestation; however there is no consensus on what population subsets will respond well to which treatment.
- The next challenge is in translating the results seen in pre-clinical models into human clinical trials.
- Last but not least. Efficacy endpoints in trials are problematic. Pain trials generally use subjective patient reported outcomes, and these are susceptible to a high degree of variability. Placebo effects in OA trials are high and persist over several months. Structure modification trials are hindered by a combination of slow and unpredictable disease progression and relatively insensitive efficacy markers, including inadequate choice of imaging to assess drug efficacy.
One solution to this is to perform larger and longer clinical trials. Another solution is to focus efforts on better patient stratification and sub-set selection and utilize sensitive objective imaging based efficacy endpoints.
May 2017 OARSI meeting further emphasized that patient phenotyping and selection of the right efficacy biomarkers will increase chances of success. Below is a brief overview of patient phenotyping strategies and imaging biomarker role in Osteoarthritis trials.
A phenotyping of knee OA population is much better researched than OA of other joints. It is defined as ‘a collection of observable traits (i.e. aetiologic factors, risk factors) that can identify and characterize a subgroup in a defined population’.
Recent publication ‘Identification of clinical phenotypes in knee osteoarthritis: a systematic review of the literature’, by A. Dell’Isola et al identified six main sets of variables proposing the existence of six phenotypes:
1) chronic pain in which central mechanisms are prominent;
3) metabolic disease (obesity, diabetes and other metabolic disturbances);
4) Bone and cartilage metabolism (alteration in local tissue metabolism);
5) mechanical disease, and
6) minimal joint disease characterised as minor clinical symptoms with slow progression over time.
It is important that the presence of distinct phenotypes within the OA patient population suggest distinct underlying causes and mechanisms of the disease. It is critical to note, that two areas of unmet medical need in OA are pain relief and reduction of structural joint degeneration. The drugs which target to relive pain rarely show disease modifying benefits and very often the other way around. Selection of the right population subset for a drug target improves the chances of showing treatment efficacy.
There are variety of clinical and chemical markers used in measuring disease efficacy, which we do not cover here, but a 2015 Update on fNIH Biomarker Consortium project can give a good overview of these: https://www.oarsi.org/research/oa-biomarkers
Often efficacy measures in OA still relies on the changes shown in X-ray. In contrast to conventional radiography, MRI can directly visualize the articular cartilage, synovium, menisci, and other intra-articular structures important to the functional integrity of joints. Growing body of evidence suggests that use of more sensitive imaging such as MRI will better understanding of the nature of the disease and drug efficacy.
Our interest is in using the most appropriate imaging biomarkers for each patient phenotype.
Guermazi et al ‘MRI-based semi-quantitative scoring of joint pathology in osteoarthritis’ provides a good overview of semi-quantitative MRI scoring systems for knee OA, showing which Scoring System addresses which anatomical parts of the joint. The whole joint assessment can be performed with several systems: WORMS; Peterfy et al. (2004), KOSS; Kornaat et al. (2005), BLOKS; Hunter et al. (2008) , MOAKS; Hunter et al. (2011) , Meredith et al. (2011). In these systems each component of the joint such as Cartilage, lesion, osteophytes, effusion, synovitis is scored on a scale from 0 to 2,3,4 depending on the scoring system. No research has been done in comprehensive correlation of findings measured by different scoring systems.
Cartilage loss can be quantified using 2 different methodologies: automated and manual. Automated segmentation is based on various machine learning algorithms, where several segmented images needs to fed into the algorithm (a training set), afterwards the algorithm can perform segmentation automatically. Otherwise cartilage loss can be scored using semi-automated measures which would have a scale from 0 (no loss) to 3 or 4 indicating complete loss (Disler et al. (1995), Biswal et al. (2002), Sonin et al. (2002), Ding et al. (2005), Duc et al. (2007),
When it comes to quantifying Synovitis, it is critical to use the Gd based contrast agents to enhance the signal in MRI scans. Several semi-auomated systems were proposed over the years, including Rhodes et al. (2005), Pelletier et al. (2008), Baker et al. (2010), Guermazi et al. (2011), Riis et all(2016)).
Our own work in synovial quantification in knee OA population suggests high correlation between the pain, synovial inflammation and progression of OA, M Boesen et al. Osteoarthritis Cartilage 25 (2), 216-226. 2016 Dec 10.
Other scoring systems for specific joint structures such as ligaments, BML and meniscus are for Ligaments: Crema et al. (2011), Stein et al. (2011); BML won the 0 to 3 scale with Wang et al. (2010), Brem et al. (2008) and Felson et al. (2001) and Meniscus – Berthiaume et al. (2005).
Imaging continues to play an important role in OA clinical research, where several exciting new technologies and computer aided analysis methods are emerging to complement the conventional imaging approaches. As the next step, there is a need to gain better understanding of patient subsets and define appropriate imaging and associated scoring techniques to address the efficacy of novel treatments.