Given the task definition, example input & output, solve the new input case.
This task is about creating an unanswerable question based on a given passage. Construct a question that looks relevant to the given context but is unanswerable. Following are a few suggestions about how to create unanswerable questions:
(i) create questions which require satisfying a constraint that is not mentioned in the passage
(ii) create questions which require information beyond what is provided in the passage in order to answer
(iii) replace an existing entity, number, date mentioned in the passage with other entity, number, date and use it in the question
(iv) create a question which is answerable from the passage and then replace one or two words by their antonyms or insert/remove negation words to make it unanswerable.
Example: Passage: In 1763, Spain traded Florida to the Kingdom of Great Britain for control of Havana, Cuba, which had been captured by the British during the Seven Years' War. It was part of a large expansion of British territory following the country's victory in the Seven Years' War. Almost the entire Spanish population left, taking along most of the remaining indigenous population to Cuba. The British soon constructed the King's Road connecting St. Augustine to Georgia. The road crossed the St. Johns River at a narrow point, which the Seminole called Wacca Pilatka and the British named "Cow Ford", both names ostensibly reflecting the fact that cattle were brought across the river there.
Output: Who owned Cuba after the Eight Years War?
This question appears to be relevant to the passage as both involves words such as 'Cuba' and 'War' which also exist in the passage. The passage mentions that "after the war, almost the entire Spanish population left, taking along most of the remaining indigenous population to Cuba". This information is not sufficient to conclude that which country owned cuba.

New input case for you: Passage: Larger drugs (>500 Da) can provoke a neutralizing immune response, particularly if the drugs are administered repeatedly, or in larger doses. This limits the effectiveness of drugs based on larger peptides and proteins (which are typically larger than 6000 Da). In some cases, the drug itself is not immunogenic, but may be co-administered with an immunogenic compound, as is sometimes the case for Taxol. Computational methods have been developed to predict the immunogenicity of peptides and proteins, which are particularly useful in designing therapeutic antibodies, assessing likely virulence of mutations in viral coat particles, and validation of proposed peptide-based drug treatments. Early techniques relied mainly on the observation that hydrophilic amino acids are overrepresented in epitope regions than hydrophobic amino acids; however, more recent developments rely on machine learning techniques using databases of existing known epitopes, usually on well-studied virus proteins, as a training set. A publicly accessible database has been established for the cataloguing of epitopes from pathogens known to be recognizable by B cells. The emerging field of bioinformatics-based studies of immunogenicity is referred to as immunoinformatics. Immunoproteomics is the study of large sets of proteins (proteomics) involved in the immune response.
Output:
What kind of immune response do smaller drugs provoke?