Lifecycle management in pharmaceutical analysis: How to establish an efficient and relevant continued performance monitoring program.

It is the objective of a systematic and holistic Quality-by-Design approach to demonstrate and ensure that an analytical procedure is fit for its intended purpose over its entire lifecycle. Such a lifecycle approach, as proposed for a new USP General Information Chapter includes the three stages Procedure Design and Development, Procedure Performance Qualification, and Continued Procedure Performance Verification, in alignment to manufacturing process validation. A decisive component of this approach is the Analytical Target Profile, which defines the performance requirements for the measurement of a Quality Attribute as the target for selection, development and optimization of the respective analytical procedures. Although the most benefit can be gained by a comprehensive Quality-by-Design approach establishing the Analytical Target Profile in the very beginning of a drug development project, it may also be established retrospectively for analytical procedures long in routine use, in order to facilitate future lifecycle activities such as continual improvements, transfers, monitoring and periodic performance evaluations. In contrast to the first two stages of the analytical lifecycle with usually limited amount of data, the Continued Procedure Performance Verification stage offers the possibility to utilize a much more reliable data base to collect, analyze, and evaluate data that relate to analytical procedure performance. This monitoring program should be aligned as far as possible with other quality systems already in place and may include performance indicators such as Conformity (i.e. out-of specification test results with analytical root-cause), Validity (i.e. failure to meet method acceptance criteria, e.g. system suitability tests), and (numerical) analytical performance parameters (e.g. ranges for replicate determinations, control sample results, etc). In addition to the monitoring of analytical control parameters by means of control charts, average (pooled) performance parameters can be calculated. Over time, a large number of data can be included and thus the reliability of these estimates is increased tremendously. Such reliable estimates for the true performance parameters, e.g. repeatability or intermediate precision are essential to identify systematic effects (also called special cause variation) with good confidence. The intent of the analytical procedure performance evaluation is to identify substandard performance, identify root cause through investigations, and determine when additional activities are required to improve it. Examples are provided for the monitoring and evaluation of performance parameters for the compendial drug substance Furosemide and for biopharmaceutical applications.

An Artificial Intelligence Approach to Proactively Inspire Drug Discovery with Recommendations.

Artificial intelligence (AI) is becoming established in drug discovery. For example, many in the industry are applying machine learning approaches to target discovery or to optimize compound synthesis. While our organization is certainly applying these sorts of approaches, we propose an additional approach: using AI to augment human intelligence. We have been working on a series of recommendation systems that take advantage of our existing laboratory processes, both wet and computational, in order to provide inspiration to our chemists, suggest next steps in their work, and automate existing workflows. We will describe five such systems in various stages of deployment within the Novartis Institutes for BioMedical Research. While each of these systems addresses different stages of the discovery pipeline, all of them share three common features: a trigger that initiates the recommendation, an analysis that leverages our existing systems with AI, and the delivery of a recommendation. The goal of all of these systems is to inspire and accelerate the drug discovery process.

Evolving Outsourcing Landscape in Pharma R&D: Different Collaborative Models and Factors To Consider When Choosing a Contract Research Organization.

Outsourcing has become an integral part of how research and early development (R&D) is executed in biotech companies and large pharmaceutical organizations. Drug discovery organizations can choose from several operational models when partnering with a service provider, ranging from short-term, fee-for-service (FFS)-based arrangements to more strategic full-time-equivalent (FTE)-based collaborations and even risk-sharing relationships. Clients should consider a number of criteria when deciding which contract research organization (CRO) is best positioned to help meet their goals. Besides cost, other factors such as intellectual property protection, problem solving skills, value-creation ability, communication, data integrity, safety and personnel policies, ease of communication, geography, duration of engagement, scalability of capacity, and contractual details deserve proper consideration. In the end, the success of a drug discovery partnership will depend in large part on the people who execute the science.

Plain Language Summaries of Clinical Trial Results: A Preliminary Study to Assess Availability of Easy-to-Understand Summaries and Approaches to Improving Public Engagement.

BACKGROUND: Easy-to-understand, stand-alone factual summaries of clinical trial results have the potential to improve public understanding of and engagement with pharmaceutical research. The European Clinical Trial Regulation (EU) No. 536/2014 is a major regulatory initiative that will result in a large number of such plain language summaries (PLSs) posted in the public domain. Today, however, little is known about the extent to which PLSs are written and are available to the general public. OBJECTIVES: This preliminary study assessed (i) 20 top pharmaceutical companies' positions on improving transparency and commitment to disclosing trial result summaries in an easy-to-understand format and (ii) the availability of such summaries in the public domain and the ease of locating them via general web searches. METHODS: The availability of PLSs in the public domain was estimated based on the number of EudraCT technical result summaries in four disease areas: chronic obstructive pulmonary disease, asthma, meningitis, and influenza. The likelihood of PLSs being easy to find through internet search engine queries by members of the public was assessed using Google. RESULTS: All 20 sponsors had committed to improve clinical trial transparency, 17 committed to sharing PLSs with trial participants, and 14 had at least one PLS available in the public domain. A total of 99 clinical studies in these four disease areas had technical summaries posted on EudraCT between 1 January 2017 and 30 June 2020. Of these 99, 14 studies had PLSs in the public domain. A total of 12 of 14 PLSs were directly captured by search engine. However, the sponsor trial identifier or EudraCT number had to be included in the search term to locate them. Generic search terms resulted in large volumes of non-relevant results. CONCLUSION: Despite the progressive movement towards clinical trial transparency, easily accessible PLSs on clinical trials are currently scarce. The provision of a European mandate and framework for non-technical result summaries by Regulation (EU) 536/2014 will be a major step to bring about positive change.

More patient and public involvement in healthcare research will help to speed the process of making new medicines. This is known by both the regulators and the healthcare industry. The healthcare industry wants to make more information on human research studies available to patients and the public. One way to help achieve this is to write simple summaries of clinical study results. Here, we use the term plain language summary (PLS) to describe them. The PLS allows people to understand human research studies more clearly. A new law will soon make it necessary to write a PLS for every clinical study done in Europe. But, today, is the PLS being used to inform the public about clinical research studies? And what is its potential? We found only  a few researchers have already begun to write PLSs. PLSs on most studies are not available to the public. Even those PLSs on public websites are very hard to find through a Google search. To better understand the potential of PLSs we are doing more research. This research will look at what the public wants to know about these studies and how they will retrieve this information.

Quality of pharmacoeconomic research in China: A systematic review.

BACKGROUND: The number of pharmacoeconomic publications in the literature from China has risen rapidly, but the quality of pharmacoeconomic publications from China has not been analyzed. OBJECTIVES: This study aims to identify all recent pharmacoeconomic publications from China, to critically appraise the reporting quality, and to summarize the results. METHODS: Four databases (PubMed, Web of Science, Medline, and EmBase) were searched for original articles published up to December 31, 2014. The Consolidated Health Economic Evaluation Reporting Standards statement including 24 items was used to assess the quality of reporting of these articles. RESULTS: Of 1046 articles identified, 32 studies fulfilled the inclusion criteria. They were published in 23 different journals. Quality of reporting varied between studies, with an average score of 18.7 (SD = 4.33) out of 24 (range 9-23.5). There was an increasing trend of pharmacoeconomic publications and reporting quality over years from 2003 to 2014. According to the Consolidated Health Economic Evaluation Reporting Standards, the reporting quality for the items including "title," "comparators of method," and "measurement of effectiveness" are quite low, with less than 50% of studies fully satisfying these reporting standards. In contrast, reporting was good for the items including "introduction," "study perspective," "choice of health outcomes," "study parameters," "characterizing heterogeneity," and "discussion," with more than 75% of the articles satisfying these reporting criteria. The remaining items fell in between these 2 extremes, with 50% to 75% of studies satisfying these criteria. CONCLUSION: Our study suggests the need for improvement in a number of reporting criteria. But the criteria for which reporting quality was low seem to be limitations that would be straightforward to correct in future studies.