Workflow: Tabular for TA Competitive Landscape Assessment

tabular for competitive landscape assessments tabular is excellent at searching, organizing, and helping you understand the competitive landscape within a particular therapeutic area (ta) in minutes this article will cover how to set up a comprehensive ta search, including how to define your business need, how to generate and filter your search, and how to extract key landscape insights this workflow also will help answer several key biopharma business questions, including what therapies make up the core competitive set of interest? what product features define each subset within the ta? how does the client compare to other products within the landscape? step 1 define your business need before initiating a search, let’s define our project context to ensure we have the most relevant, actionable results scenario suppose we are tasked with understanding the metastatic renal cell carcinoma landscape for a potential new product launch the new product is an oral vegf tki dosed twice weekly assume that we do not have deep previous experience in this ta when beginning a search, we want to balance using broad terms that capture all possibly relevant results, while also maintaining a targeted data set that we can extract insights from to understand the market context and competitive dynamics of this space, let’s start with a broad indication search of all currently fda approved products indicated to treat renal cell carcinoma step 2 create fda label search table on the tabular home screen, select the “approved drugs” tab (more about data sources available within tabular docid\ zbveslyeznxah svvqz6z here) in the “i want to analyze drugs approved for” field, we’ll enter “ renal cell carcinoma ” we are only interested in branded products, so we will leave the generic inclusion box unchecked note your search term must exactly match the phrasing in the indications and usage section of a label to return results for now, we will leave out the “metastatic” qualifier to ensure we capture all labels with potential variation in verbiage (ex metastatic vs advanced) step 3 initial data review & filtering what does the data look like? in our initial output table, we see 28 products approved for rcc immediately, we can begin to observe some contextual information about our competitive set for example, we see that products appear to be spread fairly evenly across iv and oral roa , with sq being less common we can quickly quantify this information by opening the filter menu on the roa column (13 iv, 13 oral, 1 sq) applying a a z sort on the drug name column also reveals competitive market information, such as the availability of several branded biosimilars for bevacizumab (avastin) note adding columns docid\ dq0rcozhbvzhjmok8ch3k from this quick review, we can already begin to generate some questions to help guide our exploration of this space how have biosimilars impacted the commercial dynamics in the rcc space? is product roa evenly distributed across lines of therapy / disease progression, or are there trends? (ex oral products more often used in metastatic space?) are there other product attributes that appear to define niches in the product landscape? step 4 add & generate columns define the landscape to answer these questions and more, let’s add a few product attribute columns to rapidly get a detailed understanding of product space indications mechanism of action (moa) dosing black box warnings adding columns docid\ dq0rcozhbvzhjmok8ch3k note the addition of the dosing column will expand each product from a single row to multiple, with each row representing one indication of the product our table now shows 141 rows let’s filter the “indications” column back down to our specific ta of interest, “renal cell carcinoma” we can now use the filter tool to search and select all checkboxes for “metastatic” or “advanced” rcc using this table, we get a clear picture of the mrcc landscape, including what products are used across multiple lines of therapy, as well as in different combinations regimens (ex inlyta shown highlighted) this information is critical to understanding the clinical and commercial dynamics of this indication, and for our hypothetical client’s product as our client’s pipeline product is a vegf tki (vascular endothelial growth factor tyrosine kinase inhibitor), let’s filter the moa column to only include vegf , vascular , or tyrosine in minutes, we have filtered down to an exhaustive market basket relevant to our client’s product attributes and competitive needs from here, we may want to add more attribute columns, or adding columns docid\ dq0rcozhbvzhjmok8ch3k , download this data to excel for further analysis, or continue our market exploration with an mrcc pipeline search provide feedback so we can improve! if you encounter an issue or have questions while using this wiki or tabular itself, don't hesidate to contact & support docid\ oj gwl62 cdeuqyacpazo and send feedback to our team