Product lifecycle management through patents and regulatory strategies
Bibliographic record
Abstract
During recent years, expiry of blockbuster patents, short drug lifecycles, rising development costs, heightened health authority scrutiny, dispersing markets, increased competition and need for latest technologies have put the pharmaceutical industry under mounting pressure. Thus, product lifecycle management is a prerequisite for maximizing a product’s lifetime value, improving a company’s product development processes, using product-related information to make better business decisions, and delivering greater value to customers. In order to achieve these goals and develop regulatory strategies for product lifecycle management, the pharmaceutical company should establish an effective lifecycle management team. Incorporating patent lifecycle management in product lifecycle management greatly benefits brand companies as well as specialty pharma, drug delivery, biotech and generic companies. A pharmaceutical product’s life can be described in five distinct phases: development phase, approval phase, introduction phase, commercialization and quality management phase and decline phase. Each phase poses different challenges and provides different opportunities to be considered for fabrication of the product lifecycle management strategies. At the same time, the approach for product lifecycle management varies from country to country. A comparison of various exclusivities and time taken to review a new drug application/submission/market authorization in countries namely United States, European Union, Canada and India suggests that United States is most encouraging to employ various product lifecycle management strategies, European Union is equally good if national policies are ignored, Canada is difficult to comprehend due to stringent laws and limited exclusivity and India is among the least preferred ones, although it is an excellent outsourcing service provider for contract manufacturing and research.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.025 | 0.035 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.013 | 0.013 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.015 | 0.011 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".