Pradeep Suresh

Ontological informatics infrastructure for pharmaceutical product development and manufacturing

Informatics infrastructure plays a crucial role in supporting different decision making activities related to pharmaceutical product development, pilot plant and commercial scale manufacturing by streamlining information gathering, data integration, model development and managing all these for easy and timely access and reuse. The foundation of such an infrastructure is the explicitly and formally modeled information. This foundation enables knowledge in different forms, and best manufacturing practices, to be modeled and captured into tools to support the product lifecycle management. This paper discusses the development of ontologies, Semantic Web infrastructure and Web related technologies that make such an infrastructure development possible. While many of the issues addressed in this paper are applicable to a wide spectrum of molecular-based products, we focus our work on the development of pharmaceutical informatics to support Active Pharmaceutical Ingredient (API) as well as drug product development as case studies to illustrate the various aspects of this infrastructure.

Improving Pharmaceutical Product Development and Manufacturing: Impact on Cost of Drug Development and Cost of Goods Sold of Pharmaceuticals

It is not commonly understood that one of the largest components of the overall cost of bringing a new drug to the market is the cost of product development. Cost of product development can account for as much as 30% to 35% of the total cost of bringing a new drug to the market. Quality of product development also affects time to market and the quality of manufacturing and therefore cost of manufacturing. Investment in basic research in the science of product development and manufacturing will pay for itself through savings achievable in the cost of new drug development and in the cost of goods sold (COGS) of pharmaceutical products. In order for us to arrive at good estimates of the saving potential, one first needs to have credible estimates of the cost of new drug development and the overall COGS for pharmaceutical products.

Analysis of Manufacturing Costs in Pharmaceutical Companies

In the pharmaceutical industry, costs attributed to manufacturing are a major part of a company’s total expenses. In this paper, trends in various expense and income categories of pharmaceutical companies have been analyzed with particular emphasis on manufacturing costs to gain an insight into their relationships and how they may differ among types of pharmaceutical companies such as brand name, generics, and biotechs. The study includes data published in the annual reports of leading pharmaceutical companies from 1994 to 2005. Twenty-two pharmaceutical companies were selected based on the annual revenues. The set was further divided into three groups: brand names, generics, and biotechs. The analysis shows that, between 1994 and 2005, manufacturing costs (as a percentage of total sales) are different for the three groups of companies listed above. Additionally, each group of companies differs in how savings are leveraged strategically. The data on brand-name pharmaceutical companies also indicate that there is a strong correlation between the reduction of the cost of goods sold (COGS) and the increase in R&D expenditure. This suggests the validity of Vernon’s theory that for brand-name companies, a reduction in COGS will likely have a positive impact on investments in R&D, presumably resulting in much needed innovations and future health benefits for the society.

Toward intelligent decision support for pharmaceutical product development

Developing pharmaceutical product formulation in a timely manner and ensuring quality is a complex process that requires a systematic, science-based approach. Information from various categories, including properties of the drug substance and excipients, interactions between materials, unit operations, and equipment is gathered. Knowledge in different forms, including heuristics, decision trees, correlations, and first-principle models is applied. Decisions regarding processing routes, choice of excipients, and equipment sizing are made based on this information and knowledge. In this work, we report on the development of a software infrastructure to assist formulation scientists in managing the information, capturing the knowledge, and providing intelligent decision support for pharmaceutical product formulation.

An ontological framework for automated regulatory compliance in pharmaceutical manufacturing

Pharmaceutical manufacture is one of the most tightly legislated industries today. The industry is constantly challenged to meet the rising standards of manufacturing quality and safety through rigorous regulatory requirements. As a consequence, ensuring regulatory compliance and managing a myriad of validation documents constitutes a major informatics challenge. This study addresses this challenge by developing an ontological infrastructure to support decision making in regulatory compliance. The proposed ontological informatics system, called OntoReg, is integrated with a reasoner and a rule engine through a Java integrated development environment. The system is demonstrated through industrial case studies based on regulation examples taken from the Eudralex Guide 2007.

Onto MODEL: Ontological mathematical modeling knowledge management

Abstract In this paper we describe OntoMODEL, an ontological mathematical model management tool that facilitates systematic, standardizable methods for model storage, use and solving. While the declarative knowledge in mathematical models has been captured using ontologies, the procedural knowledge required for solving these models has been handled by commercially available scientific computing software such as Mathematica and an execution engine written in Java. The interactions involved are well established and the approach is intuitive, therefore not requiring model user familiarity with any particular programming language or modeling software. Apart from this key benefit, the fact that OntoMODEL lends itself to more advanced applications such as model based fault diagnosis, model predictive control, process optimization, knowledge based decision making and process flowsheet simulation makes it an in dispensable tool in the intelligent automation of process operations. This paper also discusses the shortcomings of existing approaches that OntoMODEL addresses and also details its framework and use.

Excipient interaction prediction: application of the Purdue Ontology for Pharmaceutical Engineering (POPE)

Abstract A drug product consists of a drug substance and one or more excipients that play specific roles in rendering desired properties to that product, from improvement of flow to control of the release of the drug substance. Inter-excipient and drug substance-excipient chemical reactions are to be avoided and formulators often use heuristics and past experience to avoid potential interactions during drug product development. Multiple tools are present to mechanistically predict chemical reactions: however their utility is limited due to the complexity of the domain and the need for explicit information. In this work, the Purdue Ontology for Pharmaceutical Engineering (POPE) was used to develop an excipient reaction prediction application that made use of structural, material and environmental information to predict reactions

Development of a Computer Support System for the Management of Regulatory Compliance of Pharmaceutical Processes

Abstract The pharmaceutical sector is one of the most tightly regulated industries today and is constantly being challenged to meet rising standards of quality. However, it still uses paper documents, spreadsheets and conventional databases for the storage and manipulation of the manufacturing and regulatory process knowledge. Furthermore, in the current industrial approach, the interpretation of the regulations (which are written at a very abstract level) into operating procedures is done manually and as an afterthought to pharmaceutical process development. This approach is error-prone, time consuming and very effort intensive as it does not take advantage of recent advances in the field of knowledge management. We have been working in the development of a computer-based support system to assist in the identification of regulatory compliance of a drug manufacturing process. OntoReg, the current prototype, encapsulates pharmaceutical process and regulation knowledge in two complementing representations: 1. OWL ontologies (a knowledge representation consisting of taxonomies of concepts and logical axioms allowing to structure those concepts and to detect inconsistencies in the resulting structure), and 1. SWRL rules (which act as constraints able to enforce values inside the concepts or create relations between them). These two components, ontologies and rules, are integrated through a Java user interface which is able to identify when a pharmaceutical process does not comply with a regulation and to suggest remedial action. The resulting OWL ontology is structured in terms of three types of concepts belonging to a Regulatory, a Process or an Abstract domain (such as Time and Parameter). OntoReg has been tested with a case study for the aspirin production process in the context of its compliance with some equipment maintenance and cleaning regulations taken from the European Union Guidance on Good Manufacturing Practice. Our approach has the potential to substantially decrease pharmaceutical process validation time (up to several weeks) and effort and thus reduce development costs and commercialization prices (a well known estimate is

OntoMODEL: Ontological Mathematical Modeling Knowledge Management in Pharmaceutical Product Development, 1: Conceptual Framework

1 million per day profit for a blockbuster drug). Furthermore, this novel approach has the potential to be extended to other regulatory applications in the future, e.g. environmental compliance.