Distributed Pharmaceutical Analysis Laboratory (DPAL): Citizen

Chapter 8

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Distributed Pharmaceutical Analysis Laboratory (DPAL): Citizen Scientists Tackle a Global Problem Sarah L. Bliese, Margaret Berta, Nicholas M. Myers, and Marya Lieberman* Department of Chemistry and Biochemistry, University of Notre Dame, Notre Dame, Indiana 46556, United States *E-mail: [email protected].

The Distributed Pharmaceutical Analysis Laboratory (DPAL) leverages the chemical testing capacity at academic institutions to determine analytically the quality of medicines collected from low- and middle-income countries (LMICs). ACS-accredited degrees in chemistry require an analytical chemistry course, which often includes the use of high performance liquid chromatography (HPLC) to identify and quantify organic substances. DPAL provides an opportunity for instructors to teach about HPLC with an additional goal of finding low quality medicines in LMICs. Participation in DPAL demands a high level of accountability and responsibility from students and instructors. Participants must demonstrate system suitability of analytical instrumentation, follow standard operating procedures, determine if medicines are compliant with regulatory specifications, keep intelligible records that can be shared with other DPAL participants or with regulatory agencies, and co-author reports to health authorities when low quality medicines are discovered. DPAL can provide unique opportunities for students in the lab to witness their diligence in scientific research trigger regulatory investigations in other countries. Ultimately, the partnership of academic institutions in DPAL generates more data about compliance breaches in medicine quality, and could allow regulatory authorities in LMICs to intervene, thereby protecting public health.

© 2017 American Chemical Society Grosse; Mobilizing Chemistry Expertise To Solve Humanitarian Problems Volume 1 ACS Symposium Series; American Chemical Society: Washington, DC, 2017.


Substandard and falsified pharmaceuticals are a world-wide problem, with the largest impact in low- and middle income countries. According to recent published findings, at least 30% of pharmaceuticals being sold in many low and moderate income (LMIC) countries of Africa, Asia, and Latin American are substandard or falsified (1). A comprehensive literature review (2) provides a nuanced picture of how heterogeneous the quality of medicines is, and how sparse the published work on this important topic. Substandard pharmaceuticals can cause drug resistance, increased mortality and morbidity, and lead to a loss of confidence in the healthcare system (3). Other concerns arise from pharmaceuticals that have been adulterated with substitute active pharmaceutical ingredients (APIs) (4) or hazardous materials such as brake fluid (5). Detection of low quality pharmaceuticals is a challenging task. Martino et al. provide a review of a wide spectrum of analysis methods--from field screening tests to sophisticated mass spectrometry and NMR experiments--that have been applied to widely-counterfeited artemisinin antimalarials (6). However, pharmacopeia assays for most dosage forms rely on high performance liquid chromatography (HPLC). The capital and operating costs associated with these analytical instruments make them a scarce resource for low- and middle-income countries. An HPLC can cost tens of thousands of dollars not including the cost of purified solvents, analytical grade balances and glassware, columns, and pharmaceutical standards that are necessary for analysis. Additionally, complex business and technological infrastructure--consistent power, a clean laboratory environment, reliable transportation of supplies, availability of trained operators as well as technicians and resources for maintenance and repairs, and proof of a high level of training and documentation--are required for sustainable operation. All of these factors make it difficult for pharmaceutical analysis to take place in low resource settings. In response to the lack of access to analytical capacity in LMICs, new approaches for surveillance of post-market pharmaceuticals are being tried (7). Chapter 9 in this Symposium Series by S. Pastakia et al. describes a risk-based screening method developed by a group of pharmacists in Kenya. This screening program is the main source of pharmaceuticals for the DPAL project described in this chapter. Briefly, covert shoppers collect samples of pharmaceuticals from small medicine shops in cities and towns across Western Kenya (Figure 1a). The samples are brought to Moi Teaching and Referral Hospital (MTRH), where the sample metadata (brand, expiration date, lot number) are transcribed onto a spreadsheet. Figure 1b shows how the samples are stored after they have arrived at the University of Notre Dame. Table 1 lists the pharmaceuticals collected in 2016. One crushed pill from each sample is analyzed with a paper analytical device (PAD) to detect falsified products (8). As shown in Figure 2, a sample of the pill is applied to the paper and twelve chemical tests are simultaneously run to generate colors that reflect the chemical composition of the pill’s active ingredients and excipients. These colors result in a unique “color barcode” that allows for identification of different pharmaceuticals. The images of the PADs are uploaded to a Dropbox site where human readers and computer image analysis programs can determine the test outcomes. If the sample does not have the expected barcode, 118 Grosse; Mobilizing Chemistry Expertise To Solve Humanitarian Problems Volume 1 ACS Symposium Series; American Chemical Society: Washington, DC, 2017.


then it is flagged as suspicious and moved higher in the queue for chemical assay. In addition, a portion of the samples are selected randomly for assay in order to detect substandard products, which are not directly detected by the PAD analysis. The University of Notre Dame serves as the clearinghouse for the Distributed Pharmaceutical Analysis Lab (DPAL). Samples are shipped to Notre Dame for analysis about once every 3-4 months from MTRH. Table 1 summarizes the number and types of samples collected within a typical year. A comprehensive sample intake protocol is conducted once the samples arrive. All samples are assigned a unique Notre Dame identification number (NDID), which is used for internal sample reporting for DPAL. Sample metadata regarding manufacturing and location of purchase is recorded in a separate spreadsheet, but is not included in the information available to DPAL participants. During sample intake, a brief physical examination of each sample is conducted to identify packaging issues that could lead to product degradation, or which indicate sloppy manufacturing. Any sample exhibiting one of the following criteria is recorded: incomplete seal of blister packs, packaging printing errors (Figure 3a), and tablets or capsules that have no stamped or printed identifiers on the pill, or have poor quality markings (Figure 3b).

Figure 1. a) Sampling locations in 2016. b) DPAL samples ready for storage after intake. All samples are stored individually in zip-top bags which are grouped by active pharmaceutical ingredient (API) type in sealed plastic boxes. After sample intake, samples are stored in a 2°C refrigerator until they are sent for analysis. Samples are shipped to participant institutions upon request; they are packed with an inventory and a cover letter indicating their research purpose. The Distributed Pharmaceutical Analysis Lab started in 2014 and involves participants from 20 institutions (Figure 4a and 4b). Four have access to DPAL for data analysis projects and 16 participants are in some phase of chemical analysis for the Distributed Pharmaceutical Laboratory project. Four of these institutions are analyzing samples, and nine are conducting system suitability experiments that are required before sample testing can occur. DPAL continues to increase its analytical testing capacity as new institutions are added and current participants complete system suitability tests. 119 Grosse; Mobilizing Chemistry Expertise To Solve Humanitarian Problems Volume 1 ACS Symposium Series; American Chemical Society: Washington, DC, 2017.


Table 1. Pharmaceuticals Collected in 2016. The pharmaceuticals collected are antibiotics, anti-malarial or anti-tuberculosis drugs. Pharmaceutical

Number of Samples

Acetaminophen

120

Albendazole

90

Amoxicillin

146

Amoxicillin-Clavulanate

88

Ampicillin

72

Anti-Tuberculosis Drugs

30

Azithromycin

101

Ceftriaxone

80

Cefuroxime

80

Cephalexin

49

Ciprofloxacin

80

Doxycycline

82

Enalapril

80

Levofloxacin

82

Losartan

45

Metformin

80

Figure 2. PAD Schematic. The powdered sample is wiped across the twelve lanes (left image) and then the bottom edge of the PAD is placed in water (center image). The water wicks up the lanes in 3 minutes, dissolving reagents stored in the twelve lanes so they can react with the sample. These reactions form a color barcode (right image) characteristic of the functional groups and materials in the sample. Other features are printed on the test card to assist in computer image analysis of the color barcode. 120 Grosse; Mobilizing Chemistry Expertise To Solve Humanitarian Problems Volume 1 ACS Symposium Series; American Chemical Society: Washington, DC, 2017.


Figure 3. a) Poor quality stamping on amoxycillin capsule. b) Amoxycillin-clavulanic acid tablets with name of one of the APIs mis-spelled on the package.

Figure 4. DPAL participants a) International Participant Map. Map data: Google. b) US Participant Map. Map data: Google. DPAL has participants from the following institutions: Andrews University, AstraZeneca, California State University Fullerton, Calvin College, Coe College, DePauw University, Ghent University, Grand View University, Hamline University, Juanita College, Kean College, Liverpool School of Tropical Medicine, Moi Teaching and Referral Hospital Eldoret Kenya, Niagara College, Saint Mary’s College, St. Edward’s University, Skidmore College, University of California Santa Barbara, University of Notre Dame, and University of San Diego. Participant institutions must have the basic instrumentation required for pharmaceutical analysis. At a minimum, participants must have access to a properly calibrated analytical balance, a high-performance liquid chromatograph with ultraviolet or photodiode array detector, software for collecting and analyzing their chromatograms, and an apparatus for degassing HPLC solvents. Additional instrumentation that is required for select methodologies is a pH meter, a refrigerator for sample storage, a -80°C freezer for preserving sample solutions, and a sonicator. Generally, Chemistry departments whose analytical laboratories offer some kind of HPLC experiment are already are equipped with the required instruments. The cost of analytical standards (secondary standards calibrated vs. USP or BP primary standards) and HPLC solvents is comparable to the cost of supplies for other undergraduate HPLC experiments, so the DPAL experiments fit into existing budgets for undergraduate analytical or instrumental analysis courses. In cases where participants needed to purchase a new HPLC column, either funding has been obtained through the home institution, or in some cases the DPAL program provided a donated column. 121 Grosse; Mobilizing Chemistry Expertise To Solve Humanitarian Problems Volume 1 ACS Symposium Series; American Chemical Society: Washington, DC, 2017.


To join the Distributed Pharmaceutical Analysis Lab, the participants must first join the DPAL Open Science Framework (OSF) project (9). The OSF site is the primary means of communication and collaboration with participant institutions. It provides access to the HPLC Methodology Manual, a continuously updated document which defines the operating procedures for carrying out pharmaceutical assays, and also provides a set of checklists and Excel spreadsheets for recording analytical results and keeping track of experiments. The OSF site allows a project to contain multiple “components” which are sub-sections of the whole project. In DPAL, each participant school has its own component site which contains their institution’s analytical information and data. Each contributor is given “read-only” access to the DPAL program site, and “read and write” access to his or her institution’s component. This allows individuals to download and review files posted to the DPAL main page. The individual who is leading analysis at the participant institution (professor or principal investigator) may grant read and write access for their institution’s site to other individuals involved in the DPAL analysis at their discretion. By accepting the invitation to the DPAL OSF site, participant researchers must agree to adhere to DPAL policies regarding data security, integrity and publishing. All contributors can upload and edit files in their own institution’s site, where their raw data and documents are stored. Final HPLC results are posted to the DPAL main site through a Google Forms interface, so no participants are able to edit the final data from another institution. This helps to protect the integrity of the project data. This set-up creates a community of institutions that are committed to a common goal. Participants are encouraged to review one another’s data and results to learn from one another. This group proof-reading encourages members to maintain high quality analysis that is imperative for reliable reporting. Publishing features of OSF provide additional data security, in addition to creating citable references for DPAL data. The registration function in OSF creates a frozen version of the entire project that can never be edited or deleted--essentially, a snapshot of the project. Registration does not change the project itself, and new data can continue to be added and edited. The archived registered versions help to ensure data security. Additionally, registered versions can be issued a Digital Object Identifier (DOI) which creates a citable entity for the project, allowing researchers to reference specific versions of the project in presentations and publications. The entire DPAL project is registered quarterly, and participants can create individual registrations for their institution’s site whenever necessary. DPAL is not a certified pharmaceutical analysis laboratory; participants perform single-tablet API assays and do not conduct full compendial testing on pharmaceutical products. The data generated by DPAL cannot be used in a legal context to conclude that a medicine is of good quality. The main goal of the project is to report suspicious samples to regulatory agencies that ARE equipped to perform compendial testing. In order to provide reliable and transparent information, DPAL requires that each participant adhere to detailed standard operating procedures (SOPs) so that uniform techniques, procedures and calculations are employed at all institutions and the materials, raw data and calculations can be traced back and checked for error. Many measures in the 122 Grosse; Mobilizing Chemistry Expertise To Solve Humanitarian Problems Volume 1 ACS Symposium Series; American Chemical Society: Washington, DC, 2017.


SOP are derived from “WHO Good Practices for Pharmaceutical Quality Control Laboratories (10),” “WHO Guidelines for Preparing a Laboratory Information File (11)” and Good Clinical, Laboratory and Manufacturing Practices: Techniques for the QA Professional (12). In addition to addressing procedural protocols, the HPLC Methodology Manual explicitly states the expectations for quality and data security. The motivations and global context for protocols are explained to emphasize the importance of adherence. Legal considerations of pharmaceutical analysis are discussed to ensure that all participants fully understand how to correctly report and present DPAL results. The analytical standard operating procedures defined in the HPLC Methodology Manual include System Suitability Requirements, analytical metrics (13), sample storage and tracking, sample preparation, column storage, conditioning and washing, and sample assay and quality control procedures. After choosing an analyte and an assay based on USP or BP methodology, the DPAL participant must demonstrate system suitability through a series of specific tests based on standards laid out in USP and USP . These tests determine whether the methodology employed and the HPLC system are working properly. The results of the system suitability tests must be within acceptable limits to be considered valid. The series of system suitability experiments evaluates the precision, linearity, accuracy and range, accuracy via spike recovery, specificity, limit of detection and lower limit of quantification for the method. Participants show their results in an Excel spreadsheet to prove that they have hit the metrics for each of these experiments. The DPAL program requires that all participants successfully complete all system suitability requirement experiments and submit the data and results for review before samples from LMICs are sent for analysis. Once the system suitability experiments have been completed, the lab does not need to perform the tests again for that analyte unless the operating parameters change significantly (for example, switching to a new brand of column, or moving the assay to a different HPLC instrument). System suitability experiments include: •

•

The precision of a method is its ability to obtain reproducible results. It is imperative that methods used for DPAL have acceptable precision to ensure that there is minimal variation between samples during analysis. To evaluate the precision of a method, one sample at 100% of the expected concentration is injected six consecutive times. The relative standard deviation of the integrated intensity for each run is calculated, and must be less than 2% to be accepted (14). There is not a precision requirement between samples but before each day’s first batch of unknowns, five injections of an external standard are made to check precision, and this calibration standard is rechecked every five samples to be sure the integration stays within the 2% RSD limit. If this calibration check fails, the five preceeding samples must be re-run. Linearity describes how well a calibration curve generated by the method follows a straight line. It is important that the linearity is properly demonstrated since sample analysis calculations require a calibration 123 Grosse; Mobilizing Chemistry Expertise To Solve Humanitarian Problems Volume 1 ACS Symposium Series; American Chemical Society: Washington, DC, 2017.


•

•

•

•

•

curve to determine the amount of API. To meet the DPAL requirements for system suitability, the y intercept must fall within the error of zero, and the R2 value should be greater than 0.98. The sample concentration should be within this linear range for analysis. The accuracy and range experiment proves that the optimized method is accurate for various sample concentrations. It is important to ensure accuracy of the method is not compromised at higher or lower concentrations which could be encountered during sample analysis. To evaluate the accuracy and range, four solutions are prepared: an overdosed sample with ~150% of the standard sample, a normal sample that is ~100% of the standard, a deficient sample that is ~35% of the normal standard sample and a blank which contains no API. The overdosed, normal, deficient, and blank samples are run in triplicate. The measured concentrations of the samples should be within 2% of their true concentration. Accuracy via spike recovery is an additional means of testing the accuracy of the optimized methodology. For this experiment, a pharmaceutical dosage form of the analyte (supplied by DPAL) is required to make the standard solution. One sample solution is prepared with an additional 30% spike of API standard. The spike is determined by comparing the sample assay to the spiked sample assay, and the percent recovery of the spike is recorded. An acceptable spike recovery is between 90-110% of the spike. This metric also has some capacity to evaluate intermediate precision because there are three independent sample preparations at each level. The specificity of the method can be demonstrated by a good spike recovery from a "dirty" matrix, such as one that contains degradation products of the API. An old sample may be used as the matrix, or one that has been thermally or chemically degraded. The samples should be run according to the spike-recovery method to calculate percent recovery. The retention times for degradation products and their resolution from the API peak should be recorded for this experiment. The retention times for this test and others should be within 0.5 minutes between runs. The limit of detection (LOD) is calculated to determine the smallest quantity detectable that is significantly different from zero. The experiment is conducted via the slope-standard deviation method (13), with the standard deviation generated by running six samples, at concentrations 2-3 times the expected LOD as estimated from the linearity plot. The system suitability requirements also include a control chart which is a record of operation (15). The control chart includes information about the peak shape, retention time, resolution and integrated intensity. This document is not only used as a record of activity for each day, but functions as a diagnostic reference when issues arise (16). In general, if a peak retention time varies by more than 30 seconds or the chromatogram fails the peak asymmetry or resolution metrics listed in the SOP, it is a sign that something is amiss with the system. 124 Grosse; Mobilizing Chemistry Expertise To Solve Humanitarian Problems Volume 1 ACS Symposium Series; American Chemical Society: Washington, DC, 2017.


After the participant institution meets the system suitability requirements, assays are performed to test pharmaceutical samples from LMICs countries. Due to the small number of tablets or capsules in each sample, the assays do not follow the normal compendial procedure, which requires pooling 20-50 pills. Instead, only one tablet or capsule is taken for analysis. For each assay, the mass of the pill or tablet and the stated API content are recorded and used as the reference for determining the total amount of active pharmaceutical ingredient in the sample. If the pill does not meet Pharmacopeia specifications (generally 90-110% of stated API content) then two additional pills are assayed. Samples that are found to be substandard or low quality are sent back to the University of Notre Dame for additional testing, and the student data (Figure 5) are included in reports to country regulatory agencies and the WHO RapidAlert program. The poor quality of the product in the top trace in Figure 5 was confirmed by analysis of pills from other packages with the same lot number. This product constituted 37% of the pool of amoxicillin-clavulanate collected in Western Kenya by our Kenyan partners in 2014-2015. The drug regulatory agency in Kenya confirmed our findings by independent lab analysis of packages of this product obtained in Nairobi. In consultation with regulators at the Pharmacy and Poisons Board of Kenya, a packaging problem was identified and brought to the attention of the manufacturer. Subsequent samples of this product have been of good quality. This outcome demonstrates that detection of a poor-quality product can lead to improving the quality of medicines without reducing access.

Figure 5. HPLC data from analysis of (bottom trace) amoxycillin and clavulanate standards, (middle trace) a good quality amoxicillin-clavulanic acid pill, and (top trace) a poor-quality amoxicillin-clavulanic acid pill. 125 Grosse; Mobilizing Chemistry Expertise To Solve Humanitarian Problems Volume 1 ACS Symposium Series; American Chemical Society: Washington, DC, 2017.


The analytical requirements for the Distributed Pharmaceutical Analysis Lab make it a versatile student project. The system suitability components of the DPAL program are challenging experiments that can serve as independent research or senior thesis projects. Alternatively, demonstration of system suitability can be done as a tag-team project in an analytical chemistry or instrumental analysis lab, in which individual students perform a share of the 40-45 necessary injections over the course of several weeks; this is a good option when only one HPLC is available. After completion of the method validation experiments, sample assays can be performed as part of a standard analytical chemistry laboratory experiment, as an undergraduate research project, or even as a chemistry club service project. Due to the multi-stage nature of DPAL, the program can easily be adapted to fit many different academic settings. The utility of the DPAL program is limited by logistic factors. Participants must carry out a time-consuming system suitability test, request samples for analysis, and upload data swiftly. The DPAL program coordinators must give feedback on the uploaded results, get samples in the mail promptly, and follow up on assay results. In our experience, these steps take 1-2 semesters. Some samples pass their expiration dates during this timeframe, which means that assay results will have much less impact on manufacturers and regulators. Dissemination of standard operating procedures and data upload and feedback through Open Science Framework has helped to speed up the steps, and we are working with OSF to develop additional tools such as video FAQ sessions, automated emails, and social media links for training, tracking and motivating participants. Although pharmaceutical analysis is a mature area of analytical chemistry, the instrumentation needed to check the quality of medicines is costly and difficult to keep running, so poor quality pharmaceuticals are a persistent and widespread problem in many LMICs countries. The Distributed Pharmaceutical Analysis Laboratory (DPAL) engages colleges and universities that maintain HPLC instruments for analytical lab courses to conduct assays of medicines from LMICs countries. The analytical tasks for DPAL participants are designed to fit into the scope and structure of instrumental analysis and analytical chemistry lab courses, and can also be carried out in the framework of undergraduate research. In order to assure that the results are reliable, each DPAL participant must demonstrate system suitability before analyzing samples. The transparency of the DPAL results is maintained by posting raw data, calculations, and results for all participants to view on Open Science Framework. Citizen scientists have already had an impact on medical care in Kenya by discovering poor quality antibiotics and reporting them to the national drug regulatory agency.

126 Grosse; Mobilizing Chemistry Expertise To Solve Humanitarian Problems Volume 1 ACS Symposium Series; American Chemical Society: Washington, DC, 2017.

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5. 6. 7.

8. 9. 10. 11. 12.

13. 14. 15. 16.

Almuzaini, T.; Choonara, I.; Sammons, H. BMJ Open 2013, 3, e002923DOI:10.1136/bmjopen-2013- 002923. Kelesidis, T.; Falagas, M. E. Clin. Microbiol. Rev. 2015, 28, 443–464. Newton, P.; Green, M.; Fernandez, F. Trends in Phamacol Sci. 2010, 31, 99–101. WHO Medical Product Alert No. 4/2015 Adverse reactions caused by Falsified Diazepam in Central Africa; 2 July 2015; Ref. RHT/SAV/MD/4/ 2015. Polgreen, L. New York Times. http://www.nytimes.com/2009/02/07/world/ africa/07nigeria.html (accessed 2 April 2017). Martino, R.; Malet-Martino, M.; Gilard, V.; Balayssac, S. Anal. Bioanal. Chem. 2010, 398, 77–92. Tran, D.; Njuguna, B.; Mercer, T.; Manji, I.; Fischer, L.; Lieberman, M.; Pastakia, S. Cardiology Clinics 2017, 35, 125–134, ISSN 0733-8651, DOI:10.1016/j.ccl.2016.08.008. Weaver, A.; Lieberman, M. Am. J. Trop. Med. Hyg. 2015, 14-0384. Published online April 20, 2015; DOI:10.4269/ajtmh.14-0384. Archived version of DPAL project as of 4/4/2017; DOI: 10.17605/OSF.IO/ TR9Y7. WHO. Good Practices for Pharmaceutical Quality Control Laboratories. World Health Organization WHO 2010, 957, 81–129. WHO. WHO Guidelines for Preparing a Laboratory Information. World Health Organization File. 2011, 961, 403–408. Anderson, A.; Maxwell, J.; Hill, H. In Application of GLP in Analytical Chemistry with Cross Reference to GMP and GCP; Carson, P., Dent, N., Eds.; Good Clinical, Laboratory and Manufacturing Practices: Techniques for the QA Professional; 2007; pp 279−303. Harris, D. Quantitative Chemical Analysis; W.H. Freeman and Company: New York, 2007; Vol. 7, pp 86. U. S. Pharmacopeia. https://hmc.usp.org/sites/default/files/documents/ HMC/GCs-Pdfs/GC_pdf_USP38/c1225.pdf (accessed 5/27/2017). Harris, D. Quantitative Chemical Analysis; W. H. Freeman and Company: New York, 2007; Vol. 7, pp 663. Schazmann, B.; Regan, F.; Ross, M.; Diamond, D.; Paull, B. J. Chem. Educ. 2009 September, 86 (9), 1085–1090.

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Distributed Pharmaceutical Analysis Laboratory (DPAL): Citizen

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