|Year : 2022 | Volume
| Issue : 5 | Page : 106-118
Significance of emerging clinical oncology endpoints in support of overall survival
Shekar Patil1, Vijay Agarwal2, HS Drupad3
1 Senior Medical Oncologist, Healthcare Global Enterprises Ltd, Bengaluru, Karnataka, India
2 Senior Consultant Medical Oncologist, Aster CMI Hospital, Bengaluru, Karnataka, India
3 Medical Affairs Manager, AstraZeneca Pharma India Ltd, India
|Date of Submission||30-Dec-2020|
|Date of Decision||04-Aug-2021|
|Date of Acceptance||07-Aug-2021|
|Date of Web Publication||24-Mar-2022|
Senior Consultant Medical Oncologist, Aster CMI Hospital, Bengaluru, Karnataka
Source of Support: None, Conflict of Interest: None
Despite a better understanding of the pathophysiology and development of newer therapeutic options, cancer remains an area with several unmet needs. Although overall survival (OS) remains a gold standard endpoint for all cancer therapies, it poses challenges such as the requirement of a long-term follow-up, a higher number of patients, and a higher financial burden. Therefore, surrogate endpoints such as progression-free survival, time to progression, duration of response, and objective response rate are being investigated and used in oncology studies. Patient-related outcomes that measure the patient's overall health, quality of life, and satisfaction in the long term are crucial surrogate endpoints considered for drug approval. Surrogate endpoints shorten oncology clinical studies and accelerate the evaluation and implementation of newer therapies. Emerging surrogate endpoints such as biomarkers, immune-related response criteria, minimal residual disease, and pathological complete response are increasingly being considered in oncology trials. Validation of surrogate endpoints enables their substitution for OS and gain market approval. The selection of surrogate endpoints for an oncology trial depends on cancer type and stage, the purpose of treatment, and expected duration of survival for the relevant disease. With the advent of individualized approach and complex study designs, the field of oncology is currently undergoing a paradigm shift. The use of newer surrogate endpoints will aid in accelerating the drug development process, making patient care for oncology more accessible.
Keywords: Emerging endpoints, endpoint validation, health-related quality of life, oncology endpoints, overall survival, surrogate endpoints
|How to cite this article:|
Patil S, Agarwal V, Drupad H S. Significance of emerging clinical oncology endpoints in support of overall survival. Indian J Cancer 2022;59, Suppl S1:106-18
| » Introduction|| |
In recent years, cancer has become a global epidemic, with cancer-related mortality as the leading cause of death in 91 out of 172 countries. GLOBOCAN had estimated approximately 18.1 million new cases of cancer and 9.5 million cancer-related deaths globally in 2018. Lung cancer was the leading cause of cancer-related deaths with 1.76 million deaths in 2018 followed by cancers of the gastrointestinal tract (colorectal, liver, and stomach) and breast. With the increased incidence of cancer worldwide, there is a growing need for effective treatment options in oncology., The past decade has seen rapid progress in our understanding of cancer biology, thus underpinning newer treatment modalities, including personalized targeted therapies and immune modulators. The accelerated translation of these therapeutic options from the clinical trial phase to the general population is required for real-world clinical benefit. In 2018, the U.S. Food and Drug Administration (FDA) approved 59 novel drugs, of which 23 belonged to the oncology domain; several drugs are currently in the pipeline for the unmet needs of patients with cancer.
The primary goal of an oncology drug is to prolong survival and improve the quality of life (QoL). A safe risk–benefit assessment, demonstrated during clinical trials, is also of paramount importance for regulatory approval of any treatment modality. Endpoints, an integral part of the clinical trial design, provide predefined and measurable outcome measures linked to patient benefit. Endpoints help quantify the effect of therapy in terms of mortality, morbidity, and functional capacity. In cancer studies, the overall survival (OS) is a gold standard endpoint as it reflects the ultimate clinical outcome of cancer therapy. However, the quantum of time involved in assessing OS in a clinical trial setting often renders it unfeasible for regulatory approval. Hence, surrogate endpoints have found a way for objectifying the clinical benefit of oncology drugs before OS data emerge. These endpoints aim at reducing the length of clinical trials, thus accelerating the approval of drugs along with providing evidence on patient-centric parameters such as QoL and overall satisfaction. In the current review, we have elaborated on the surrogate endpoints used in oncology trials, their validation in predicting the OS, and their anticipated applications.
| » Types of clinical trial endpoints and their significance|| |
The clinical endpoint is an aspect of a patient's health or clinical status measured to assess the benefit of treatment., The endpoints should be clinically relevant, sensitive to change, with an ability to be measured objectively, and should be accepted by physicians, regulatory bodies, as well as patients. The choice of an endpoint depends on the target population, disease characteristics, and the aim of therapy. For the approval of any therapy, it should improve the survival and/or reduce hospitalization or safely improve the functional capacity. Hence, clinical endpoints generally measure either of the following parameters: mortality, morbidity, or health-related quality of life (HRQoL). Clinical trial endpoints reflect an outcome of interest that is prespecified (i.e. chosen before the data are analyzed) and statistically analyzed to address a particular research hypothesis. Endpoints are categorized depending on the study, disease, and therapy characteristics.,
| » Endpoints in oncology|| |
The endpoints in oncology trials based on how a person feels, functions, or survives are known as clinical outcome assessments (COAs). The COAs are classified as patient-centric and tumor-centric endpoints owing to the nature of the disease. Patient-centric endpoints include OS and HRQoL. Tumor-centric endpoints include surrogate endpoints such as progression-free survival (PFS), biomarkers, disease-free survival (DFS), objective response rate (ORR), pathologic complete response (pCR), time to progression, and duration of response. Surrogate endpoints are used in clinical trials when direct endpoints may take a long time to be realized, lengthening the study duration. The use of surrogate endpoints needs consideration of tumor histology, line of treatment, treatment type (targeted, immune therapy, etc.), study characteristics, life expectancy, and regulatory requirements. Surrogate endpoints have a clear mechanistic rationale and provide evidence to predict specific clinical benefits. A schematic representation of endpoints in oncology is presented in [Figure 1]. A summary of direct and surrogate endpoints with their advantages and disadvantages is discussed in subsequent sections.
| » Patient-centric endpoints|| |
OS represents the time from clinical trial randomization until death from any cause and is measured in the intent-to-treat population. It measures direct clinical benefit as median OS (duration of trial for which 50% of subjects are alive) or as a percentage of patients alive at particular time points (OS at 1, 2, or 5 years). The OS is precise, easy to measure, documented by the date of death, not subject to investigator bias, and the outcomes are mutually exclusive. However, it is associated with longer follow-up, the need for randomized controlled studies, and requires a large population to be studied. Longer follow-up increases not only the study duration but also the cost of clinical study and medication. Longer study duration also leads to more patient dropouts leading to information bias (due to patients lost to follow-up), and the need to include a larger population in the study. The inability to conduct studies with crossover therapy is another limitation of evaluating OS as an endpoint. In addition, with increasing treatment options becoming available after the failure of one therapy, OS benefit might be confounded. These reasons have led to the development and use of surrogate markers in oncology clinical trials.
Patient-reported outcomes (PROs) include measurement of patients' experience based on the symptom burden, mood, physical function, distress, or QoL. PROs are fast becoming one of the crucial surrogate endpoints for drug approval in oncology given the importance of patient's overall health and satisfaction in the long term. PROs measure the status of a patient's health condition without amendment or interpretation of the patient's response by a clinician or a designee. It can include reports from the spouse, significant other, or primary caregiver in circumstances where the patient may not be able to provide appropriate information. PROs provide evidence in addition to clinical information on the same condition from patients' perspectives. PROs have been approved as secondary or even primary endpoints in oncology clinical studies. Some examples of landmark studies where PRO-based endpoints (QoL) were used to assess clinical benefit are mentioned in [Table 1].
|Table 1: Randomized trials utilizing patient-reported outcomes as endpoints in cancer,|
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Numerous efforts are being made to collect the PRO data electronically (ePRO), especially data on patient safety. Several studies have evaluated the feasibility of collecting PRO data electronically.,,,, An ePRO collection is a reliable and accurate tool to monitor adverse events and can be used to alter the treatment given to the patients, improving compliance. Rare or unheard adverse events that are unexpected by the clinicians may also be revealed by ePROs., The ePRO data are expected to enhance the quality of care received by patients with cancer.
In spite of these advantages, logistical and technological challenges may hamper the use of PROs in clinical trials. Implementation of PROs requires appropriate training of staff, physicians, and patients. The time lag between disease progression and appearance of symptoms and unavailability of blinding are the other challenges reported with PROs.
| » Tumor-centric surrogate endpoints|| |
Clinically validated tumor-based surrogate endpoints are used more in oncology trials, allowing the therapy to reach patients sooner. Surrogate markers are based on the response of the tumor to the treatment and are clinically relevant enough to drive the treatment course in clinical practice.
In recent clinical trials, PFS has gained importance as it provides direct information on the effect of the drug. PFS is the time between treatment assignment and tumor progression or death resulting from any cause in the metastatic stage. The disease progression is usually assessed on the basis of radiological reports. PFS is easy to measure, objective, and reached earlier. Another advantage of measuring PFS is that it is not biased by postprogression therapies. But the prolongation of PFS may or may not be significant in terms of OS as trials have demonstrated improved PFS with no improvement of OS., Immunotherapy trials where pseudoprogression (delayed response following a temporal apparent progression) or hyperprogression (rapid progression after treatment administration) may be observed during the phase when immune cells infiltrate the tumor pose a challenge to evaluating PFS., Thus, for evaluating the effectiveness of immunotherapy, standard definitions of progression, which is tumor progression immediately at the time of documenting a new lesion or a predefined increase in the calculated tumor burden, might not be applicable. Taking into account this unique mechanism of action of immunotherapy, newer surrogate endpoints and response criteria, immune-related response criteria (irRC), have been designed. They are discussed in detail in the subsequent sections of the review.
Another surrogate endpoint in some oncology trials is DFS. DFS is defined as the time from randomization until tumor recurrence or death from any cause after treatments given with curative intent. DFS has similar advantages as evaluating PFS. [Table 2] summarizes the tumor-based surrogate endpoints with their advantages and disadvantages.
| » Biomarkers|| |
A biomarker is an objectively measured and evaluated indicator of normal biological processes, pathogenic processes, or pharmacologic responses to a therapeutic intervention. A biomarker is any substance, structure, or process that can be measured in the body or its products and influences or predicts the incidence of outcome or disease. Biomarkers can be used as a predictive link for COAs. A validated biomarker can be used for the accelerated approval of drugs and has become critical to rational drug development. Biomarkers are defined per their applications in subtypes.
A diagnostic biomarker detects or confirms the presence of a disease or condition of interest or identifies an individual with a subtype of the disease. An example of a diagnostic biomarker is serum prostate–specific antigen levels in prostate cancer. These levels can be used for disease diagnosis as well as redefining disease classification. The use of molecular testing in many cancer types has changed the course of therapy over the past years. Validation of diagnostic bio-markers are very important as it can change the treatment course.
Monitoring biomarkers are used for serial measurement of assessment of a disease or medical condition for evidence of exposure to a medical product or environmental agent, or to detect an effect of a medical product or biological agent like monitoring of circulating cell-free DNA for cancer progression. They may overlap with other subtypes of biomarkers. They are important for ensuring patient safety during the clinical study and make decisions about thresholds, continuing, or concluding the therapy in clinical practice.
A predictive biomarker predicts an individual or group of individuals more likely to experience a favorable or unfavorable effect from the exposure to a medical product or environmental agent; for example, human epidermal growth factor receptor-2 (HER2) positivity in breast cancer suggests efficacy with trastuzumab. Multiple predictive biomarkers, mostly based on a single gene/protein, are currently in Phase 2 or Phase 3 evaluation along with their companion therapeutic agents., A prognostic biomarker is used to identify the likelihood of a clinical event, disease recurrence, or disease progression in patients with a disease or medical condition of interest. Prognostic biomarkers are applied to set the inclusion/exclusion criteria for clinical studies and for predicting the risk of an event or poor outcome in an individual. A predictive biomarker gives information about the effect of therapeutic intervention, whereas a prognostic biomarker provides estimates of the patients with overall cancer outcomes., After exposure to a drug or therapy, prediction of the presence or extent of toxicity can be determined through the use of safety biomarkers. A classic example of a safety biomarker is the prolongation of QT interval on electrocardiogram with arrhythmic drugs. Along similar lines, a biomarker that predicts the risk of developing a disease in an otherwise healthy individual is a susceptibility/risk biomarker. Genetic testing for breast cancer (BRCA gene test) falls under this subtype.
A clear correlation between the measured level of biomarker and the clinical outcome is a prerequisite for the biomarkers to be considered as surrogate markers. Establishing the correlation might pose a challenge in oncology because of the multifaceted nature of the disease and biomarker evading mechanisms demonstrated by the cancer cells. Hence, composite biomarkers are now increasingly sought to be used as surrogate markers instead of a single biomarker., Combination of a specific clinical event plus an increase in cancer antigen-125 in ovarian cancer to define disease progression is an example of composite endpoints in oncology. Composite endpoints should have an adequate representation of all its components in assessing the efficacy and should not rely exclusively on improvement in a single component.
| » Emerging tumor-related endpoints|| |
In some cancers, circulating tumor cells might provide evidence of disease status and have a predictive as well as prognostic value in therapy.,,, In metastatic breast cancer, quantification of circulating tumor cells is correlated with patient survival and clinical outcomes. Similarly, in prostate cancer, circulating tumor cells are better predictors of response compared with prostate-specific antigen concentration. Circulating tumor DNA (ctDNA) is another technique used for measuring response in ovarian and colorectal cancer.,, The potential advantages of ctDNA include ease of access and early assessment of response, but the limiting factor for incorporating these as endpoints is the lack of validation and reproducibility of the results.
With the advent of immunotherapy, in which the mechanism of action is different from standard chemotherapy regimens, several shortcomings were observed while evaluating the treatment response with the help of response evaluation criteria in solid tumors (RECIST) criteria. Therefore, newer irRC endpoints were designed for immunotherapy regimens. The irRC pattern of evaluation takes into account the transient increase in tumor size, apparent worsening of clinical signs because of action of immune cells on the tumor, and delayed response on radiologic assessment observed during immunotherapy. The irRC includes the surrogate endpoints such as immune-related complete response, immune-related partial response, immune-related stable disease, and immune-related progressive disease, which can be utilized to evaluate the efficacy of immunotherapy in cancer patients.,
Minimal residual disease (MRD) is another emerging concept to evaluate liquid tumors. MRD takes into account the residual tumor burden after standard laboratory tests are unable to quantify the cancer cells. It applies highly sensitive technologies and is an indicator of long-term survival and short- and long-term toxicities. It has become an important endpoint for hematologic tumors and an independent predictor of prolonged event-free survival (EFS).
Evaluating the central nervous system (CNS) response of the drugs is particularly important in the case of tumors with CNS metastases, like lung cancer. The CNS acts as a sanctuary site for tumor cells, as the majority of anticancer drugs cannot cross the blood–brain barrier., Additionally, the chance of CNS metastases increases as the median survival of cancer patients increases. Common endpoints evaluating CNS activity of drug are CNS ORR, using RECIST, which sets the criteria for assessing tumors seen on imaging, CNS disease control rate (defined as the number of patients with stable disease, a partial response, or a complete response divided by the number of evaluable patients).
pCR is used to assess the efficacy of neoadjuvant therapies in oncology. In certain subpopulations, patients continue to be at substantial risk of recurrence and death postoperatively. The efficacy of neoadjuvant therapy in these patients is analyzed by pCR, which demonstrates the absence of residual invasive cancer and in situ cancer in tissue and all sampled regional lymph node specimens.
With increasing therapeutic options in second- and third-line settings for cancer, Time to Second Objective Disease Progression (PFS2) is gaining importance. It is defined as the time from randomization to objective tumor progression on next-line treatment or death from any cause. The European Medical Agency (EMA) recommends the use of PFS2 to understand gain in PFS, where OS cannot be evaluated. In a literature review, PFS2 was employed as a secondary endpoint in 14 trials. Of the 14 trials, 13 had PFS2 as their primary endpoint. PFS2 is thus emerging as a crucial secondary endpoint in a growing number of clinical oncology trials assessing the benefits of maintenance or sequential therapy in the palliative setting.
| » Validation of surrogate endpoints|| |
Although some endpoints may serve as a surrogate for OS, only validated surrogate endpoints can be considered as a substitute for clinical outcomes and used for marketing approval of a therapy. Joffe and Greene proposed four approaches for the validation of surrogate endpoints and to evaluate the extent of surrogacy. These four approaches are as follows: (1) a “proportion-explained” approach, (3) an “indirect effects” approach, (3) a “meta-analytic” approach, and (4) a “principal stratification” approach. Clinical trials often need to demonstrate a strong association between the surrogate endpoints and the clinical outcomes. Based on their association with clinical outcomes, surrogate endpoints can be classified as a candidate, reasonably likely, and validated [Table 3]. Depending on the level of evidence, the hierarchy of the surrogate endpoint is decided. The regulatory authorities accept only appropriately validated endpoints to make approval decisions for the drug.,
| » Choice of oncology endpoints other than OS|| |
For an endpoint to be selected in a cancer study, factors such as cancer type, the purpose of treatment, or expected duration of survival are to be considered. Although OS is the final goal of any treatment, the obvious disadvantages of using OS as the primary endpoint necessitated the use of other surrogate endpoints in clinical studies. In an analysis of historical data from 1952 to 2016, 71 therapies were approved for 10 cancer types. Of these, approximately 59% of the therapies were approved before the OS data were provided, 11.6% were approved after evidence for OS was provided, the rest 29.6% were approved in the same year the evidence for OS was provided. The mean duration from the presentation of OS data and drug approval was 4.9 years (range 15–46 years). This difference in approval duration might be because of treatment goals and survival time associated with the goals in different cancer types. The OS evidence for breast cancer and colorectal cancer is usually published years after a drug is initially approved (median approval duration are 4.0 and 2.5 years, respectively), whereas for melanoma and liver cancer, approval duration tends to be shorter (median approval distance is 0 years for both). The discrepancy in the time required for the availability of OS might be because of the good prognosis in some cancer types as well as the availability of several lines of therapy, which result in a longer time required for accumulating the survival data.
With the development in drugs and trial design, endpoints should be selected appropriately, as inappropriate endpoints may result in an effective therapy not being approved. For example, gefitinib was initially approved for third-line therapy for non–small-cell lung carcinoma (NSCLC) as 10.6% of patients showed good response. When evaluated for second-line therapy in the subsequent trials, it did not show significant OS benefit. However, when efficacy data were evaluated using appropriate biomarkers and epidermal growth factor receptor mutation testing, gefitinib was approved in the second line, and eventually as first-line therapy as one of the preferred therapy options., Conversely, the use of surrogate endpoints in clinical trials has led to accelerated approval of drugs for use in clinical practice, but with the evolution of evidence against survival benefit, the drug might be withdrawn for the indication. For example, bevacizumab plus weekly paclitaxel in metastatic breast cancer showed a 6-month improvement in median PFS compared with paclitaxel alone; hence, it was provided accelerated approval by the FDA. But further studies did not show improvement in OS; hence, the FDA withdrew the approval subsequently.
The cancer type and stage play a very important role in evaluating the correlation between surrogate endpoint and OS. In the case of adjuvant therapy in pancreatic cancer studies, a meta-analysis showed a weak correlation between DFS and OS (Spearman rank correlation coefficient = 0.31). In another meta-analysis, it was shown that DFS and PFS are good surrogate endpoints for OS in colorectal, lung, and head and neck cancers, whereas DFS is highly correlated with OS in gastric cancers. In the case of ovarian cancer, the therapy options have evolved, and hence there are multiple lines of therapy available postprogression. This makes calculation of OS difficult, and OS may also be biased because of postprogression treatment. Thus, in cases where postprogression survival (PPS) is longer, PFS and other intermediate clinical endpoints such as PFS2 should be considered along with OS. In case of advanced, recurrent ovarian cancer, a systematic review, including 39 trials, evaluated the correlation between surrogate endpoints and OS. The ORR was strongly correlated with PFS compared with the disease control rate (DCR; r = 0.82 vs. 0.58). On further analysis, it was found that a 10% increase in the ORR increased the PFS by 1.2 months and the OS by 2.83 months, thus reducing the hazard ratio for OS by 2.5%.
In NSCLC patients, crossovers and postprogression treatment play an important role in evaluating the correlation between OS and ORR or PFS. In a systematic review, in clinical trials with crossovers and postprogression treatment bias, the correlation between PFS and ORR with OS was poor (correlation coefficient = 0.18 and 0.25, respectively). In trials that did not allow crossover and reported balanced postprogression treatments, the correlation coefficients of ORR and median progression free survival (mPFS) with median overall survival (mOS) were 0.528 and 0.778, respectively. In such cases with a possible postprogression treatment bias, PPS, which is highly associated with OS after the first and second lines of therapy, can be used as a surrogate endpoint instead of PFS.,
In another meta-analysis of studies on advanced metastatic breast cancer, PFS and response rates were reported as primary endpoints in 38% and 32% of studies, whereas OS was reported as the primary endpoint in only 7% of studies. Similarly, although traditionally, durable complete remission and EFS are considered surrogate endpoints for leukemia trials, newer endpoints such as MRD are being considered for surrogacy. A list of endpoints based on which approvals for some drugs were provided recently is mentioned in [Table 4]. Over years, survival data have been decreasingly used for drug approvals and time to events (PFS, DFS, and EFS) and response rates are gaining importance for drug approval. [Figure 2] shows the changing paradigms in the FDA approval of drugs based on endpoints.
|Figure 2: Historical distribution of the U.S. FDA (Food and Drug Administration)-approved drugs,|
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| » Future perspectives for surrogate endpoints in oncology|| |
Oncology studies are currently becoming more complex with several adaptive and seamless study designs such as basket trials (patients with the same specific mutation or biomarker), platform trials (evaluation of different targeted therapies for one disease), and umbrella trials (evaluation of multiple targeted therapies for one disease or multiple diseases) getting incorporated. These clinical designs evaluate multiple outcomes in a single trial. Consequently, newer endpoints are anticipated to emerge. These types of studies have the added advantage of reducing the study duration and utilizing endpoints related to time to events such as PFS or EFS. These data on clinical benefits might assist the regulatory agencies in providing conditional approval or accelerated approval for drugs. More comprehensive studies such as randomized clinical trials can be followed on postapproval for procuring data on OS and granting complete approval. After approval, real-world evidence studies and big data analysis are expected to play a significant role in the field of oncology. The analysis provides greater and more accurate information on patient safety in long-term use. Patient-reported data are bridging the analytical gap between the objective data from electronic health records (EHRs) and the subjective experience of individual patients. For example, the data on the clinician's assessment of improvement in symptoms during the therapy can be assessed along with the improvement in QoL reported by the patients. This will result in obtaining comprehensive information about the efficacy of a therapy. This development will aid in developing newer strategies in cancer management.
| » Conclusion|| |
The field of oncology is currently undergoing a paradigm shift as newer therapeutic options are being developed. Although the ultimate goal of every therapeutic option remains enhancing the OS, the treatment goals are now incorporating measures to improve patients' QoL. With the advent of drugs having a distinct mechanism of action compared with conventional medications, clinical studies have become more complex. Hence, there is a need for establishing newer surrogate endpoints to decrease the time required for drug approvals.
Regulatory authorities have recognized several surrogate endpoints on the basis of which accelerated approval can be sought, and subsequently, OS can be studied during the postmarketing phase. Ideally, the use of surrogate endpoints should be limited to cancer types, where the surrogate has demonstrated a strong correlation with OS and has the ability to predict the clinical benefit. Because of the emphasis being laid on individualized therapy, endpoints should also be designed per the therapy; a “one-size-fits-all” technique can no longer be applied to cancer studies. Hence, existing endpoints should be periodically reviewed and newer endpoints should be validated to ascertain the clinical relevance of old endpoints and replace them with newer, more robust ones. The time has come for the oncology community to accept and develop new, validated endpoints that reflect clinical benefits. Striking the right balance between the principal and surrogate endpoints is of paramount importance to provide relevant information on the benefit and risks of a new treatment, which in turn not only affects regulatory approval of new drugs but also increases patient access to newer treatments.
The authors would like to thank AstraZeneca Pharma India Ltd. for the development of this manuscript in collaboration with Ms. Prajakta Nachane, M. Pharm. from Covance Scientific Services and Solutions Pvt. Ltd. in accordance with the GPP3 guidelines (http://www. ismpp.org/gpp3).
Financial support and sponsorship
AstraZeneca Pharma India Ltd.
Conflicts of interest
Drupad HS is an employee of AstraZeneca Pharma India Ltd.
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[Figure 1], [Figure 2]
[Table 1], [Table 2], [Table 3], [Table 4]