TOWARDS A HOLISTIC RISK MODEL FOR SAFEGUARDING THE PHARMACEUTICAL SUPPLY CHAIN: CAPTURING THE HUMAN-INDUCED RISK TO DRUG QUALITY

Counterfeit, adulterated, and misbranded medicines in the pharmaceutical supply chain (PSC) are a critical problem. Regulators charged with safeguarding the supply chain are facing shrinking resources for inspections while concurrently facing increasing demands posed by new drug products being manufactured at more sites in the US and abroad. To mitigate risk, the University of Kentucky (UK) Central Pharmacy Drug Quality Study (DQS) tests injectable drugs dispensed within the UK hospital. Using FT-NIR spectrometry coupled with machine learning techniques the team identifies and flags potentially contaminated drugs for further testing and possible removal from the pharmacy. Teams like the DQS are always working with limited equipment, time, and staffing resources. Scanning every vial immediately before use is infeasible and drugs must be prioritized for analysis. A risk scoring system coupled with batch sampling techniques is currently used in the DQS. However, a risk scoring system only allows the team to know about the risks to the PSC today. It doesn’t let us predict what the risks will be in the future. To begin bridging this gap in predictive modeling capabilities the authors assert that models must incorporate the human element. A sister project to the DQS, the Drug Quality Game (DGC), enables humans and all of their unpredictability to be inserted into a virtual PSC. The DQG approach was adopted as a means of capturing human creativity, imagination, and problem-solving skills. Current methods of prioritizing drug scans rely heavily on drug cost, sole-source status, warning letters, equipment and material specifications. However, humans, not machines, commit fraud. Given that even one defective drug product could have catastrophic consequences this project will improve risk-based modeling by equipping future models to identify and incorporate human-induced risks, expanding the overall landscape of risk-based modeling. This exploratory study tested the following hypotheses (1) a useful game system able to simulate real-life humans and their actions in a pharmaceutical manufacturing process can be designed and deployed, (2) there are variables in the game that are predictive of human-induced risks to the PSC, and (3) the game can identify ways in which bad actors can “game the system” (GTS) to produce counterfeit, adulterated, and misbranded drugs. A commercial-off-the-shelf (COTS) game, BigPharma, was used as the basis of a game system able to simulate the human subjects and their actions in a pharmaceutical manufacturing process. BigPharma was selected as it provides a low-cost, time-efficient virtual environment that captures the major elements of a pharmaceutical business- research, marketing, and manufacturing/processing. Running Big Pharma with a Python shell enables researchers to implement specific GxP-related tasks (Good x Practice, where x=Manufacturing, Clinical, Research, etc.) not provided in the COTS BigPharma game. Results from players\u27 interaction with the Python shell/Big Pharma environment suggest that the game can identify both variables predictive of human-induced risks to the PSC and ways in which bad actors may GTS. For example, company profitability emerged as one variable predictive of successful GTS. Player\u27s unethical in-game techniques matched well with observations seen within the DQS

Innovative Computational Methods for Pharmaceutical Problem Solving a Review Part II: Serious Gaming

Serious gaming has begun to take a foothold in pharmaceutical problem-solving. Companies such as Akili\u27s Interactive are seeing success in the form of positive clinical trial results and FDA approval of digital therapeutics. Academic researchers have begun exploring novel uses for serious gaming in the way of protein design and more with promising results. This paper provides a review of such topics in addition to topics of game repurposing- repurposing a game originally intended for entertainment into a serious game-such as Minecraft and America\u27s Army. Reviewing these topics this paper shows the utility of serious gaming as a tool for problem-solving while empathizing its applications in the pharmaceutical industry. Further, serious gaming’s utility in other sectors is briefly discussed before concluding. It is suggested that serious games will be a powerful scientific tool both in terms of educational, training, and therapeutic purposes moving forward

A Brief Review of FDA’s Novel Tools for Ensuring Pharmaceutical Quality in the Human Drug Supply Chain

Purpose Pharmaceutical manufacturers execute quality control operations and Good Manufacturing Practices (GMP) to provide safe drugs. The Federal Drug and Food Administration (FDA) is tasked with ensuring manufacturers are performing such procedures. Faced with limited resources the FDA has developed novel tools to aid pharmaceutical manufacturing oversight. This paper briefly reviews these tools. Methods Current inspection approaches employed by the FDA are identified by searching the FDA’s guidances, the Code of Federal Regulations, public reports and other online resources. Outcomes Industry A risk-based site selection model (SSM) is used to prioritize on-site inspections for FDA investigators. Theoretically, the SSM allows FDA investigators to focus on critical firms that are at high risk of failing to meet quality standards. Analytical testing of drugs is performed by FDA laboratories as well as manufacturers’ laboratories. Despite this, two of the highest profile recalls in the last couple years (valsartan and ranitidine) were not initially identified by the FDA. Instead, Valisure, an online pharmacy that tests each batch of inventory, detected the issues. Physicians and Consumers The FDA has provided easy-to-use online tools for patient and physician reporting of drug quality problems. The FDA has also created consumer education campaigns to aid in protecting patients from fraud and counterfeiting. Conclusion The FDA has developed novel methods of redistributing their workforce to maximize product quality and consumer safety with limited resources. The methods include a risk-based SSM for prioritizing on-site inspections, providing education tools, and online reporting of quality problems. FDA laboratories also provide analytical testing to ensure purity standards are met. The recent publicized discoveries of Valisure are leading other pharmacies such as the University of Kentucky Central Pharmacy to begin testing incoming drugs. It is critical for these pharmacies and the FDA to cooperate to protect the pharmaceutical supply chain

Methods for Using NHANES for Exposure Estimates

The National Health and Nutrition Examination Survey (NHANES) is a scheme of studies with the objective of measuring the health and nutritional status of adults and children in the United States. The survey is unique in that it has a clinical component (NCT00005154), and combines interviews and physical examinations into its data sets. NHANES is an important program of the National Center for Health Statistics (NCHS). NCHS is a unit of the Centers for Disease Control and Prevention (CDC) and has the responsibility for producing vital and health statistics for the United States. Data from the NHANES studies can be used to produce exposure estimates of the population to foods, food ingredients, additives, dietary supplements, and in some years of the surveys, other items like allergens, prescription medications, endotoxins, cigarettes and alcohol. This paper is about methods for using NHANES to establish exposure estimates for food ingredients and additives. Sample Octave/Matlab code is provided, but other software can be used with the NHANES databases as well

Innovative Computational Methods for Pharmaceutical Problem Solving a Review Part I: The Drug Development Process

Computational methods have provided pharmaceutical scientists and engineers a means to go beyond what\u27s possible with experimental testing alone. Providing a means to study active pharmaceutical ingredients (API), excipients, and drug interactions at or near-atomic levels. This paper provides a review of this and other innovative computational methods used for solving pharmaceutical problems throughout the drug development process. Part one of two this paper will emphasize the role of computational methods and game theory in solving pharmaceutical challenges

Monetary Incentives for Producing Counterfeit, Adulterated, and Misbranded Medicine: Case Studies and Examples

Background: Pharmaceutical fraud can be very profitable. Those working in pharmaceuticals are in a tempting position as the nature of the product and supply is complex, making detection of fraud difficult and expensive. However, a reliable pharmaceutical supply can often be a life-or-death situation for patients. Thus, when detection of fraud occurs, a Regulator\u27s Dilemma often emerges (recall a drug for which a supplier is the sole source, or allow a substandard product to be sold)—generally resulting in pharmaceutical companies receiving minimal penalties even for the worst acts. Despite pharmaceutical companies\u27 unique leverage over regulators and profitability, studies are rare in the scientific literature regarding pharmaceutical fraud. Purpose: The primary aim of this article is to increase awareness of the various types of pharmaceutical frauds. In addition, the secondary objective is to provide insight into the influence economics possesses in motivating pharmaceutical fraud. Method: Case studies and examples of pharmaceutical fraud are described. Reviewed case studies include purchasing and distribution of products from unlicensed sellers, unlawful promotion of Paxil, Wellbutrin, and Avandia, and concealing bladder cancer risk associated with pioglitazone. Economic information is gathered through mining the US Department of Labor Statistics, Govinfo, US Securities and Exchange Commission, companies annual reports, and US Department of Justice databases. Economic screenshots are used to summarize the frauds surrounding economics both within and external to the companies. Results: Purchasing and distribution of products from unlicensed sellers occurred between December 2006 to August 2009 and took place solely in the US. Economic snapshots of this time show that the US was in an economic recession. During this time raw material costs were high and the pharmaceutical industry was experiencing major lay-offs. The scheme resulted in the company grossing over $50 million-dollar in added proceeds. The unlawful promotion of Paxil, Wellbutrin, Avandia, Avair and others occurred between 1998 to 2010 on a global scale. The economic snapshot of this time shows the company faced patent expiration of several highly profitable patents during this period, starting in 1997 with Zantac’s expiry and then with the loss of Augmentin in 2002, several years before its patent expiration of 2018. Avandia lost market exclusivity in 2008 and Advair lost market exclusivity in 2010. The company also faced several regulatory challenges. In addition, more than one economic recession occurred during this time including the Asian and US markets. Finally, the concealment of cancer risks with pioglitazone occurred from the approval of the drug in 1999 to the settlement in 2015. Between 1995-1999 the company was growing and had launched several worldwide ventures. In addition to expanding, the company faced regulatory black-box challenges as well as encountering recessions in the US. Conclusions: History suggests that monetary incentives are motivators in unethical behavior and fraud cases. Economic recessions, patent expirations, and company expansion are among the most consistent economic pressures surrounding the cases studied, suggesting these variables may be predictors of potential drug quality issues

An Unfinished Canvas: Allocating Funding and Instructional Time for Elementary Arts Education

An Unfinished Canvas found that California's elementary schools face unique challenges inproviding all students with sequential, standards-based arts education. In particular, elementary principals identified inadequate funding and insufficient instructional time as significant barriers to the provision of arts education. For this study, we sought to further understand the impact of funding and time on elementary arts education. To do so, we examined the allocation of funding and instructional time in 10 schools across five states (Kentucky, Massachusetts, Minnesota, New Jersey, and California)

Population Data-Driven Formulation of a COVID-19 Therapeutic

This study is designed to utilize computer modeling of the US population through the ongoing CDC NHANES trial to reduce the need for preclinical formulation and toxicology studies of an Ebola antiviral (BSN389) being repurposed for COVID-19, and to thereby speed the candidate therapeutic to the clinic. BSN389 has poor solubility in water and that limits its bioavailability. BCD greatly increases the solubility of BSN389 and results in high bioavailability of BSN389 in animal studies. The goal of this study is to determine the current daily exposure of the population to beta cyclodextrin (BCD), and then keep the amount of BCD in the formulation below that level, so drug use does not add significantly to BCD exposure of human subjects. Data from the Centers for Disease Control (CDC) National Health and Nutrition Examination Survey (NHANES) are combined in this computational experiment with data on international BCD shipments from ingredient manufacturers to evaluate exposure of the US population

Experimental and Mathematical Tools to Predict Droplet Size and Velocity Distribution for a Two-Fluid Nozzle

Despite progress in laser-based and computational tools, an accessible model that relies on fundamentals and offers a reasonably accurate estimation of droplet size and velocity is lacking, primarily due to entangled complex breakup mechanisms. Therefore, this study aims at using the integral form of the conservation equations to create a system of equations by solving which, the far-field secondary atomization can be analyzed through predicting droplet size and velocity distributions of the involved phases. To validate the model predictions, experiments are conducted at ambient conditions using water, methanol, and acetone as model fluids with varying formulation properties, such as density, viscosity, and surface tension. Droplet size distribution and velocity are measured with laser diffraction and a high-speed camera, respectively. Finally, an attempt is made to utilize non-scaled parameters to characterize the atomization process, useful for extrapolating the sensitivity analysis to other scales. The merit of this model lies in its simplicity for use in process control and optimization