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7/31/2019 Potential Use of Drug Response-Efficacy Biomarkers for Predicting LifeThreatening Disease Causing Side Effects of

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Potential Use of Drug Response-Efficacy Biomarkers for Predicting Life-Threatening Disease Causing Side Effects of Therapeutic Drugs

Drug response-efficacy biomarkers, excluding drug targets, can be used 1) for predicting the clinical

outcome in patients, 2) for identifying responders and non-responders to specific drug therapy, 3) forselecting patients for alternative treatment methods, 4) for optimizing personalized drug treatmentregimen (drug dose, drug treatment schedule etc.,) and 5) for predicting potential side effects, which aregenerally identified using specific biomarkers, for nephotoxicity, cardiotoxicity, or hepatoxicity, expressedin response to a specific drug treatment. Changes in the level of expression of these toxicity biomarkersare currently used for predicting potential side effects of therapeutic drugs. Drug response-efficacybiomarkers may also include surrogate biomarkers, surrogate endpoint biomarkers and certainpharmacodynamic biomarkers, which can also be used for predicting potential side effects of drugs,including disease causing side effects, in addition to its use in identifying pharmacological activity ofdrugs. Though drug response-efficacy biomarkers are powerful tool in differentiating responders fromnon-responders to a specific treatment and can be used for developing diagnostic tools for personalizedtherapy, the question remains that can these biomarkers give clues to future onset of drug therapy

induced life-threatening diseases in patients? This question came from two major observations 1) for thesame drug therapy distinct drug response-efficacy biomarkers have been identified in different patientsand 2) there are conflicting reports on side effects of a specific drug therapy from different patient studies.If drug response-efficacy biomarkers can be used for predicting disease causing side effects of drugtreatment, we might be able to develop smart biomarkers, which can reduce morbidity and mortality inpatients, and can revolutionize the personalized medicine approaches. Intelligent data mining andcomprehensive data analysis might provide valuable information that can be translated into meaningfulpredictive models. In this exploratory analysis, we took anti-TNF therapy response markers of rheumatoidarthritis (RA) as an example to demonstrate that drug response-efficacy biomarkers can be potentiallyused for predicting potential disease causing side effects of a specific drug treatment.

Analysis using anti-TNF therapy response biomarkers in rheumatoid arthritis (RA) patients

Integrin alpha-X (CD11c or ITGAX) expression in monocytes is a potential candidate biomarker foridentifying the efficacy of anti-TNF therapy, as reported in adalimumab montherpay, in rheumatoidarthritis (RA) patients (1,2). Increased expression of CD11c was identified as a potential biomarker forselecting adalimumab therapy responders, CD11c biomarker was not associated with methotrexate(MTX) therapy alone or in combination with adalimumab (1). CD11c is largely expressed in monocytesand granulocytes where it is important in monocyte adhesion and chemotaxis, and is involved in cell-to-cell interaction during inflammatory responses. Monocytes can differentiate into monocyte-deriveddendritic cells (DCs) or inflammatory DCs (iDCs) upon upregulated expression of the CD11c together

Posted online on September 3, 2012. Link to original blog:http://www.sciclips.com/sciclips/blogArticle.do?id=1020&blog=Potential%20Use%20of%20Drug%

20Response-Efficacy%20Biomarkers%20for%20Predicting%20Life-

Threatening%20Disease%20Causing%20Side%20Effects%20of%20Therapeutic%20Drugs


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with MHC and co-stimulatory molecules under certain inflammatory conditions. (3). Based on our

preliminary data extraction and data mining, we found several CD11c linked functional roles that areassociated with diseases other than RA (Table 1). Analysis of these data indicates that CD11c may beassociated with metabolic disorders, infectious diseases and the risk in developing cardiovasculardiseases such as atherosclerosis, which has been shown to be associated with RA (3).

Table. 1: Potential association of CD11c expression with human diseases


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Though the risk in developing cancers, especially lymphoma, hepatosplenic T-Cell Lymphoma (HSTCL),

nonmelanocytic skin cancers and melanoma, from anti-TNF therapy in RA patients has been welldocumented (20,21,22,23), the reports on the effect of anti-TNF therapy in developing cardiovasculardiseases are conflicting. There are few reports showing the negative association of anti-TNF antagonistswith congestive heart failure, heart failure and atheroscleorsis in patients with RA (24, 25). On the contrary,majority of the reports have shown improvement in atherosclerosis and other cardio vascular diseases in RApatients following anti-TNF therapy (26,27,28,29, 30, 31, 32, 33, 34, 35, 36, 37). It is important to point outthat these independent study results were based on small number of patients for a limited period of time (6 -18 months), which may not be ideal for studying the development of cardiovascular diseases (CVDs) likeatherosclerosis.

It is evident from the above mentioned studies that some RA patients who were treated with anti-TNFtherapy may develop cardiovascular diseases (CVDs) and other patients may show signs of improvement in

CVDs associated with RA. In this scenario, identification of biomarker(s) that can differentiate high riskpatients from low risk patients may have significant impact on anti-arthritis drug therapy. Patients responseto a drug treatment might depend on several known and unknown cellular, genetic, epigenetic orphysiological factors, which are not detectable during pre-clinical studies using cell based or animal modelscreening studies. However, the drug response biomarkers, along with physical and anatomical markers,that are identified during clinical trials and follow-up studies may provide valuable information that can beused for predicting potential side effects of therapeutic drugs. In order to test this argument, we took theanti-TNF drug response biomarker CD11c and analyzed, by intelligent data mining and functional mapping,the potential of this biomarker for predicting the risk of developing atherosclerosis in RA patients. Thefunctional role of CD11c in developing atherosclerosis has been well studied in case of postprandial lipemia(high fat-diet). The pathway of CD11c in atherosclerosis development is shown in Fig. 1. This pathwayanalysis shows that high-fat diet can induce CD11c overexpressing monocytes that can bind to VCAM-1

causing VCAM-1 arrest and accumulation in atherosclerotic lesions (38). During hypertriglyceridemia,monocytes internalize lipids, upregulate CD11c, and increase adhesion to VCAM-1 (39). The question iswhether CD11c overexpressing moncytes generated from anti-TNF therapy can also have similar effect inpatients? Detailed analysis of the data generated from patient studies may be required to confirm this.However, the role of CD11c monocytes resulting from high-fat may provide some indications on the use ofanti-TNF therapy along with high-fat diet or in patients with obesity, diabetes, hypochloertermia and othersimilar conditions associated with cardiovascular diseases. Moreover, studies need to be conducted to testwhether the expression of CD11c during or after anti-TNF therapy is associated with an increased risk indeveloping cardiovascular complications in RA patients with obesity, diabetes, hypochloertermia etc.Significant increase in total cholesterol, HDL cholesterol and triglycerides levels was reported to beassociated with anti-TNF-alpha treatment in RA patients (40).

Moreover, CD11c expression has been reported as a drug response-efficacy biomarker for drug treatmentsother than anti-TNF agents. Decreased expression of CD11c was reported as a drug efficacy biomarker inpsoriasis patients treated with phosphodiesterase 4 (PDE4) inhibitors (41), whereas, increased expressionof CD11c in circulating leukocytes was reported during Raptiva (anti-CD11a, Efalizumab) therapy inpsoriasis patients (42). Additionally, CD11c was shown to be associated with inflammatory papulesdeveloped during Raptiva therapy (42) and increase in CD11c+ myeloid DCs was associated with psoriasis


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might be possible since several anti-TNF response biomarkers have been reported in RA patients. Some of

the biomarkers include, elevated expression of an anti-atherogenic protein CD36 (47), decreased levels ofcartilage oligomeric matrix protein (COMP)-C3b complexes ((48), decreased salivary IL-1beta (49),calprotectin expression (50), IgM antibodies against phosphorylcholine (anti-PC) (51) . Further, biomarkersassociated with combination therapy with anti-TNF agents and methotrexate (MTX), have also beenreported. Some of these biomarkers include decreased IL-17 expression (52), increased plasma type I IFNlevels (53),and increased expression of TH2 receptors, chemokine receptor CCR4 and IL-4R (54).Interestingly, some of the above-mentioned biomarkers, such as CD36 or anti-PC, are anti-atherosclerosisrelated proteins and others, like calprotectin, are associated with atherosclerosis , cardiovascular disease ,acute myocardial infarction, multiple sclerosis, cancer and infectious diseases (55, 56,57)(Fig. 2). Thus,multiple biomarkers associated with anti-TNF therapy can have varying functional roles in the developmentof CVDs and other diseases (Fig. 2). Increased expression of type 1 IFNs in response to combinationtherapy with MTX and anti-TNF agents may be an indication that these patients are susceptible to

atherosclerosis. MTX therapy was shown to reduce cardiovascular risk by upregulating reverse cholesteroltransport (RCT) proteins, ATP-binding cassette transporter A1 (ABCA1) and 27-hydroxylase, which aredownregulated by COX-2 inhibitors, a known inducer of atherosclerosis (58, 59, 60, 61). IFN-gamma wasshown to inhibit RCT by decreasing the expression of 27-hydroxylase and ABCA1 (62), and probably,increased expression of IFN-gamma in RA patients treated with the combination of MTX and anti-TNF mayhave increased risk in developing CVDs. Detailed analysis of the various biomarkers in patients combinedwith intelligent data mining will open up possibilities for analyzing and predicting disease causing sideeffects of various drugs that may ultimately pave paths for developing most efficient personalized medicinestrategies. In order to achieve this goal, we might need to consider adopting new concepts in biomarker


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discovery and translational utilization of these biomarkers for personalized treatments. This means, current

methods of biomarker selection and validation need to be revised.

A model for identification, validation and clinical utilization of drug response-efficacy biomarkers

Biomarkers are identified, selected and validated based on the specificity and sensitivity of any givenbiomarker in detecting a disease in question. This means, a valid diagnostic biomarker should bestatistically significant in detecting that disease and an ideal biomarker should have high specificity,sensitivity and prevalence (positive predictive value (PPV) or precision rate). The question is whether weshould adopt similar approaches for the identification and validations of drug response-efficacy biomarkers,which can vary significantly, in terms of expression as well as the types of biomarkers, from patient topatient depend on known or unknown genetic or physiological make up of a patient. It may not be surprisingif drug response-efficacy biomarkers were found to be associated with gender, race, geographical location

and other gene-social environmentfactors. Therefore, it may be difficult to find a single or commonbiomarker as a drug response-efficacy biomarker, which can identify responders from non-responders. Incontrast, variations in drug response-efficacy biomarkers may indicate the use of these biomarkers forpredicting the severity or complexity of diseases and potential drug induced future side effects, which can beanalyzed with the help of intelligent data mining and data analysis.

Based on these observations, we propose a model for the identification, validation and clinical utilization ofdrug response-efficacy biomarkers (Fig. 3). According to this model, each and every drug response-efficacybiomarkers identified in clinical trials, excluding biomarkers identified using cell or animal model studies,should be carefully selected and validated based on pre- and post-therapy expression analysis along withpatients clinical, phenotypic, physiologic, anatomical, environmental and genetic characteristics. A simplepathway analysis may not be sufficient to predict the impact of these biomarkers in disease causing side

effects, rather global data mining and comprehensive data analysis should be performed to understandfunctional roles of drug response-efficacy biomarkers. Accordingly, every drug efficacy biomarkers that aresignificantly associated with the drug treatment, whether it is positive in one or few patients, should beconsidered as a valid or valuable biomarker, which should be then evaluated beyond clinical trials, thismeans, the analysis of drug response-efficacy biomarkers should be a continuous process where patientsamples are continuously monitored and evaluated during the prescription based treatment process. Thedata generated from such continuous large-scale analysis will help in identifying and validating reliable drugresponse-efficacy biomarkers, which can be used for developing robust diagnostic/treatment methods andfor predicting potential life-threatening side effects of therapeutic drugs. This will ultimately help indeveloping an efficient and reliable personalized medicine strategy. The importance of each and everybiomarker is evident from the two similar definitions of a biomarker - A biological marker orbiomarker isdefined as a characteristic that is objectively measured and evaluated as an indicator of normal biologic

processes, pathogenic processes, or biological responses to a therapeutic intervention. A biomarker can bea physiologic, pathologic, or anatomic characteristic or measurement that is thought to relate to someaspect of normal or abnormal biologic function or process. (Source: US FDA (63), and, A biomarker is abiological characteristic that can be objectively measured and that serves as an indicator of normalbiological processes, pathogenic processes, or responses to therapeutic interventions. Biomarkers can bebroadly characterized into 3 groups: those that measure physical or genetic traits (anthropometric indexes,metabolic gene polymorphisms), those that measure chemical or biochemical agents in the biologicalsystem (plasma retinol, iron, zinc), and those that assess a measureable physiologic function (test of nightvision, cognitive assessment) or future clinical risk (64).


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Finally, the choice of biomarker identification method is very critical in the discovery of drug response-efficacy biomarkers considering the limited availability of patient derived samples. Cell or animal modelbased systems for drug response-efficacy biomarkers discovery may not be a suitable method mainly due tothe fact that these model systems will not mimic the real-world patient response to a drug therapy.Exploratory methods such as proteomic approaches may not be a viable option for the identification ofbiomarkers since this technology has severe limitations such as complex sample preparation steps, sampleloss, lack of reproducibility, laborious, expertise-dependency, mass spectrometry detection limitations etc.

Immunological or molecular assays, including next generation sequencing methods, that are robust andinvolve minimal sample preparation steps and sample loss need to be considered for identifying drugresponse-efficacy biomarkers using patient samples.

Note: This scientific blog is a contribution from Sciclips Consultancy(http://www.sciclips.com/sciclips/consultancy.do) team.


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References

References are hyperlinked to respective abstracts or full articles. Please click the reference numbers to thecitation details.

Link to original blog:

http://www.sciclips.com/sciclips/blogArticle.do?id=1020&blog=Potential%20Use%20of%20Drug%

20Response-Efficacy%20Biomarkers%20for%20Predicting%20Life-

Threatening%20Disease%20Causing%20Side%20Effects%20of%20Therapeutic%20Drugs

potential use of drug response-efficacy biomarkers for predicting lifethreatening disease causing...

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