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Lieberman paper test card linked in version

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Prof. Lieberman's presentation at the 5th Latin American Future Trends in Medicine meeting describes a new method for testing medicines in the field with paper test cards. The presentation has been shortened by removing some videos.

Transcript

  1. 1. Finding fake medicines Encontrar medicamentos falsos Prof. Marya Lieberman Department of Chemistry and Biochemistry University of Notre Dame Notre Dame, IN 46556 mlieberm@nd.edu con mi amigo, Google Translate
  2. 2. Drug discovery vs. drug delivery low quality antimalarial medicine causes 122,000 deaths each year in kids under 5 ¿POR QUÉ? Renschler et al. 2015 Am. J. Trop. Med. Hyg. Genuine coartem cure rate for malaria: 97% (genuina Coartem cura la malaria) Falsified coartem (top) antimalarial tablet contains <4% API (y falsa no cura nada) Coartem can be assayed by HPLC in 15 minutes with a compendium assay El análisis químico (HPLC) puede detectar todos fármacos de baja calidad
  3. 3. • Has any person at your table ever found a falsified medication? What type of medicine and how did they find it? • Hay alguna de las personas en su mesa que se ha encontrado un medicamento falsificado ? ¿Qué tipo de medicamento y cómo se encuentran ?
  4. 4. if you see a this…there must be a that si usted ve un “esta” ... debe haber una “que” Interpol 2014 low risk for manufacturers profit attracts criminal enterprise Kenya 2013 Poor regulatory infrastructure lack of testing capacity Uganda 2013 supply chain problems
  5. 5. constraints and prior solutions Shimadzu HPLC at Moi U Broken for past 5 years sitting unused in a corner of Ghanaian FDA broken instruments no trained staff no supply chain Trying to buy glassware in Eldoret, Kenya Constraints for field tests: fast easy cheap no power no instruments no lab equipment no handling of chemicals results available/archived limitaciones de las pruebas: rápido fácil barato ninguna energía no hay instrumentos sin equipo de laboratorio sin manipulación de productos químicos resultados disponibles / archivado
  6. 6. Analytical challenges Many medicines have similar structures 90-120% 343 WHO Essential Medicines Dosage forms are mixtures (insoluble excipients, fixed-dose combinations) Substandard formulations are common Pills and tablets are solids
  7. 7. Wax printing • hydrophobic wax defines channels • reagents are stored in paper • passive pumping through capillary action • filter, mix, or separate based on channel geometry Yagoda, H. Ind. Eng. Chem. 1937, 9, 79–82; Carrilho, Martinez, and Whitesides, Anal. Chem. 2009, 81, 7091–7095 El papel se imprime con tinta cerosa y después se calentó para fundir la cera. Es una forma barata de hacer que los dispositivos de microfluidos.
  8. 8. Paper analytical device (PAD)
  9. 9. These color tests can identify drugs that have very similar structures Estas pruebas de color pueden identificar fármacos que tienen estructuras muy similares i. Beta lactam + amide (basic copper test) ii. Primary amine (ninhydrin) iii. Phenol (diazo coupling)
  10. 10. Ampicillin Amoxicillin Benzyl Penicillin Unknown/desconocido Reading the color bar code Cómo leer el código de barras de color Weaver et al. Analytical Chemistry, 2013, 85 (13), pp 6453–6460 Amox Amox Compare each lane to known standard Comparar cada carril de ejemplo conocido
  11. 11. How many drugs can the PAD detect? ¿Cuántos medicamentos puede detectar el PAD? 12 60 drugs give distinctive bar codes Analgesics: Acetaminophen Antipyrine Aspirin Dipyronea Gabapentene Naproxen Oxycodoneb Tramadolc Antibiotics: Amoxicillin Ampicillin Azithromycin Cephalexin Cefdinir Ciprofloxacin Doxycycline Erythromycinc Levofloxacin Minocycline Nitrofurantoin Oxytetracycline Penicillin G Streptomycin Antihistamines: Cetirizine Chlorpheniraminea Diphenhydramine Doxylamine succinate R-phenylephrine Antimalarials: Amodiaquine Lumifantrine Pyrimethaminec Quininec Chloroquine Malanil Mefloquinec Primaquine PDE-5 inhibitors Tadalafil Sildenafilc Vardenafil Diet drugs/stimulants: Caffeine Sibutraminec Phenterminec Phenolphthalein Excipients: Starch, Talc Carbonate (chalk, calcite, baking soda) Polyethylene glycolc TB medications: Ethambutol Isoniazid Pyrazinamide Rifampicin IE, RIP(E) Misc. Albendazolec Dextromethorphanb Tamiflud Metformin Evista, Lyrica ferrous sulphate hydroxyzine pamoate Sevelamer Telmisartan Heroin Crack cocaine Methamphetamine
  12. 12. What’s in a falsified medication? Active Pharmaceutical Ingredient (API) + → PharmaceuticalExcipient(s) • Adulterants/fillers • chalk • maize meal • gypsum • talcum powder • No API • Incorrect dose API • Wrong API • Degraded API
  13. 13. Please form 12 teams For each team: Para cado equipo: • 6 PADs • pure isoniazid (INH) • 4 “unknown” samples • wood sticks (palitos) • small water dishes (platillos para el agua)
  14. 14. 1) Run pure INH and pure water 2) Run 4 unknowns Demo Test card containing preloaded dry reagents Tarjeta de ensayo que contiene precargado reactivos secos Step 1: Crush tablet and apply to card You should see powder in each lane Paso 1 : se aplica el polvo a la tarjeta Usted debe ver el polvo en cada carril Step 2: Dip card in water for 3 minutes Paso 2 : Coloque el borde inferior de la tarjeta en agua durante 3 minutos Colors develop in 1-3 minutes colores aparecen en 1-3 minutos sample name sample name sample name sample name Use wooden stick to wipe powder firmly across the paper Isoniazid (INH) Instrucciones de uso del PAD
  15. 15. B, G, H: Orange-red (rojo anaranjado C, F, I: Green (verde) J: Starch gives black color that does not move El almidón da color negro que no se mueve * Does the sample contain INH? (Hay alguna INH?) * Does the sample contain anything that shouldn’t be there? (¿Hay algo sospechoso?)
  16. 16. B, G, H: Orange-red (rojo anaranjado C, F, I: Green (verde) J: Starch gives black color that does not move El almidón da color negro que no se mueve * Does the sample contain INH? (Hay alguna INH?) * Does the sample contain anything that shouldn’t be there? (¿Hay algo sospechoso?) 1 INH 30% Starch 70% 2 Antipyrine 50% Rice Flour 50% 3 Paracetamol 4 INH
  17. 17. B, G, H: Orange-red (rojo anaranjado C, F, I: Green (verde) J: Starch gives black color that does not move El almidón da color negro que no se mueve * Does the sample contain INH? (Hay alguna INH?) * Does the sample contain anything that shouldn’t be there? (¿Hay algo sospechoso?) 1 INH 30% Starch 70% 2 Antipyrine 50% Rice Flour 50% 3 Paracetamol 4 INH
  18. 18. Sensitivity: if it’s there, do you see it? Ideal = 100% Sensibilidad: si está allí , ¿lo ves? Ideal = 100 % 0 10 20 30 40 50 60 70 80 90 100 e- rich phenols Starch Talc Tertiary amines Baking Soda Chalk (med-heavy) Acetaminophen Pyrazinamide Isoniazid Rifamicin Ethambutol Ampicillin Amoxicillin Beta-lactam Specificity: if it’s not there, do you not see it? Ideal = 100% Especificidad: si no está ahí , ¿no lo ves ? Every pure API detected with 92-100% sensitivity and 88-100% specificity but reading by eye requires expert readers Pero esto requiere lectores expertos the logistics won’t work at scale
  19. 19. What the user does not see 2 3 5. Geo-tracking4. Image Analysis Time GoodSamples 6. Archiving and Monitoring Data collection with cell phones 1
  20. 20. Image analysis goals: • Classification: Assign test images to correct class based on stored “training” images Clasificación : Asigne imágenes de prueba para corregir clase basada en imágenes de "entrenamiento “ • Quantification: Measure color ratios or intensities Cuantificación : Mida las proporciones de color o intensidad • Adaptive learning: Search for patterns in data sets Aprendizaje adaptativo : Búsqueda de patrones en conjuntos de datos
  21. 21. Fixing bad pictures/corregir malas fotos “wild type” image rotation, keystoning, shadows, color distortion Image re-sized, aligned, lanes identified Prof. Pat Flynn and Prof. Chris Sweet
  22. 22. Color bar code Computer image analysis Compare unknowns to stored “authentic” bar codes using neural network. Asigne imágenes de prueba para corregir clase basada en imágenes de "entrenamiento “ • trained on 20 samples each of Acetaminophen, Acetylsalicylic Acid, Amodiaquine, Amoxicillin, Ampicillin, Artesunate, Calcium Carbonate, Corn Starch, Diethylcarbamazine, Ethambutol, Isoniazid, Rifampicin, Tetracycline • Tested with 10 samples each of same drugs (N=130) Sandipan Banerjee and Chris Sweet
  23. 23. Computer can classify accurately Counterfeit Drug Detection with Paper Analytical Device Images using Deep Learning; S. Banerjee, J. Sweet, C. Sweet, WACV 2016 submitted Acetaminophen Aspirin Amodiaquine Amoxycillin Ampicillin Artesunate CaCO3 Corn Starch DEC Ethambutol Isoniazid Rifampicin Tetracycline Acetaminophen5/10 Aspirin10/10 Amodiaquine10/10 Amoxycillin10/10 Ampicillin3/10 Artesunate3/10 CaCO36/10 CornStarch9/10 DEC10/10 Ethambutol10/10 Isoniazid10/10 Rifampicin10/10 Tetracycline10/10 Actual active ingredient (and number classified correctly) Howsurewastheneuralnetwork?
  24. 24. Color tests can detect “fillers” Pruebas de color se pueden detectar sólidos insolubles • talc eosin red dye  cherry • starch, flour I2  blue/black • chalk, baking soda, calcite Fe(III)  Fe2O3 Weaver et al. Analytical Chemistry, 2013, 85 (13), 6453–6460
  25. 25. Pharmaceuticals in “herbal” medicines Productos farmacéuticos en medicamentos “a base de hierbas" Samples from Israeli Ministry of Health— Division of Enforcement and Inspection “Ingredients:Mulberry leaf extracts, jobstears seed, medical amylum”
  26. 26. A B C D E F G H I J K L Phenolphthalein laxative use was banned in 1999 Samples from Israeli Ministry of Health— Division of Enforcement and Inspection Sibutramine anorexiant banned in 2010 “herbal” medicine medicamentos “a base de hierbas"
  27. 27. Quantification is hard • API range 90%-120% “meets standard” 50 70 90 110 130 150 170 190 100% Chloroquine (CQ) 70% CQ, 30% chalk 40% CQ, 60% starch 0% CQ, 100% aspirin 0% CQ, 100% chalk Min Outlier Max Outlier Chloroquine (CQ) 70% CQ, 30% chalk 40% CQ, 60% starch 0% CQ, 100% aspirin 0% CQ, 100% chalk Weaver et al, AJTMH 2015 Colorintensity
  28. 28. Iodometric titration on a paper card degradation products eg RSH add known amount of I2 thiosulfate/starch on test card performs back-titration KOH 20 minAmoxicillin (este PAD cuantifica el agente de yodación de la sal)
  29. 29. Quantification of beta lactams via USP <425> degradation products eg RSH add known amount of I2 back-titration with thiosulfate/starch KOH 20 minAmoxicillin
  30. 30. degradation products eg RSH add known amount of I2 thiosulfate/starch on test card performs back-titration KOH 20 minAmoxicillin N. Myers, unpublished data 50% API 75% API 90% API 95% API 100% API Substandard antibiotics don’t react with all the iodine. The more I2 is left over, the more dots turn blue Antibióticos deficientes no reaccionan con todo el yodo . El más yodo sobra , más puntos se vuelven azules
  31. 31. Can we analyze medicines in the real world? ¿Podemos analizar los medicamentos en el mundo real?
  32. 32. Question for every table Pregunta para cada mesa • If you could test the quality of five medicines in your country, which five would you pick, and why? • Si se pudiera probar la calidad de cinco medicamentos en su pais, los cuales cinco elegirías y por qué?
  33. 33. Pharmacists at Moi Hospital and staff at Kenyan Pharmacy and Poisons Board (KPPB) chose ampicillin, amoxycillin, amoxycillin/clavulanate, ciprofloxacin, and azithromycin 401 brands in Kenya O.M.G. Very low quality missing API <50% API substitute API serious risk to patients and public health Substandard does not meet pharmacopeia standards risk of harm to patients and public health Good quality Meets pharmacopeia standards
  34. 34. Secret shoppers Compradores secretos PADAdverse reactions Reacciones adversas Confirmatory HPLC HPLC confirmatorio Kenyan Pharmacy and Poisons Board
  35. 35. User training, Eldoret, Kenya
  36. 36. PADs run in Kenya one of these things is not like the other ones una de estas cosas no es como las demás
  37. 37. Confirmatory analysis by HPLC: Amoxycillin 500 mg, clavulanate 125 mg Suspicious Normal Standards Rebecca Ryan
  38. 38. Impacts Secret shoppers buy antibiotics at pharmacies in Western Kenya Kenyan Pharmacy and Poisons Board 4 reports to KPPB 2 reports to WHO Screen medicines at MTRHAdverse drug reactions from MTRH clinics HPLC at ND 167 assays, 57 substandard of which 14 lacked an API
  39. 39. Publicity bbc.com/news/health-32938075 bbc.com/news/health-32982539 [video file is >100 MB]
  40. 40. Outreach
  41. 41. Urinary iodide PAD Nutrition AirPAD air pollutants BioPAD (with Goodson) food quality ? PAD Detect falsified medicines Iodized salt SaltPAD
  42. 42. Graduate students: Nicholas Myers, Sandipan Banerjee, James Sweet, Dr. Abigail Weaver, Jamie Luther Undergraduate students: Kate Girdhar, Margaret Berta, Esseatim Etim (Winthrop), Sarah Bliese (Hamline), Rebecca Ryan, Steven Froelich, Hannah Reiser, Kellie Radell, Eliza Herrero, Leah Koenig Collaborators: Sonak Pastakia, Rahki Kharwa, Mercy Maina, Celia Ngetich, Phelix Were, and Beatrice Jakait (AMPATH/MTRH Kenya) Chris Sweet, Center for Research Computing, UND Cheryl Fleurer and Mark Witkowski, FDA/FCC Deanna O’Donnell, Hamline U. SBPD Mickey Arieli, IMOH $$: University of Notre Dame, Global Alliance for Improved Nutrition, Lilly Endowment Bill & Melinda Gates Foundation US-AID DIV program

Editor's Notes

  • artemisinin drugs Nobel 2015
  • Sub-saharan africa: avg. per capita total health expenditures $32 (US $6,719)
    countries that have functional MRAs 135 /218
    WHO analysis of 26 MRAs in sub-saharan Africa:
    17 countries had a functional QC laboratory, 5 of those labs had documentation of their analysis procedures.
    Only 5 countries had a systematic post market testing plan; 7 tested samples when someone complained.
    8 countries collected adverse effects reports, but they didn’t act effectively on the results.

    More pictures—sad sack pictures
  • Strengths—Everything is included; orthogonal testing methods (color tests + TLC); small scale so less chemical handling; commercially available, high penetrance/distribution; detects substitute ingredients and lack of API
    Weaknesses—not quantitative, need consumable supplies, testing one substance can take hours, high knowledge/skill level required to carry out & interpret tests, ambiguous results common
    >4,000 euros + consumable supplies, 1-2 weeks training

  • Transition: need to analyze solid dosage forms (consider the constraints of the analysis…)

    falsified formulations may include unexpected excipients or APIs…

    Qualitative analysis can reveal discrepant formulations (eg trout in the milk)

    quantitative analysis of API will show more substandards
  • about this time there was a paper by whitesides describing how to print wax on paper to make sophisticated devices for enzyme assays (DFA)
  • maybe use video instead
  • fix beta lactam

  • ----- Meeting Notes (10/16/15 13:32) -----
    780 cards (read 1-4 lanes per card)
  • 24 lane PAD

    Nothing from praziquantel, mebendazole

    simplifies logistics
  • Sustaining chemistry in the developing world will lead to monitoring over geographical areas over time.
  • AT least the neural network knows when it knows something!!

    transition: so far we have looked at detection of single, pure APIs. What about dosage forms?
  • ability to detect insoluble excipients is valuable (thoreau’s trout in the milk)
  • pharmacists at Moi Hospital and KPPB wanted to study five common medicines in Western Kenya

    I did a brand survey
    81 paracetamol,
    108 amoxicillin,
    72 amoxy-clav,
    89 ciprofloxacin,
    51 azithromycin
  • This is a question about values!

  • I did a brand survey
    81 paracetamol,
    108 amoxicillin,
    72 amoxy-clav,
    89 ciprofloxacin,
    51 azithromycin
  • Minilab—713 in use, started in 1997, 22 very low quality products showcased on their website

    458 samples from Kenya sent to US in 2014
  • vision: make cheap cards that could be used in a low resource setting by untrained people
    will produce some visual readout, it will be complicated if we want to find out complex questions, but we can get around that…
    Make use of the mobile phone network to archive/interpret data
    large scale data collection
  • logos
  • Description

    Prof. Lieberman's presentation at the 5th Latin American Future Trends in Medicine meeting describes a new method for testing medicines in the field with paper test cards. The presentation has been shortened by removing some videos.

    Transcript

    1. 1. Finding fake medicines Encontrar medicamentos falsos Prof. Marya Lieberman Department of Chemistry and Biochemistry University of Notre Dame Notre Dame, IN 46556 mlieberm@nd.edu con mi amigo, Google Translate
    2. 2. Drug discovery vs. drug delivery low quality antimalarial medicine causes 122,000 deaths each year in kids under 5 ¿POR QUÉ? Renschler et al. 2015 Am. J. Trop. Med. Hyg. Genuine coartem cure rate for malaria: 97% (genuina Coartem cura la malaria) Falsified coartem (top) antimalarial tablet contains <4% API (y falsa no cura nada) Coartem can be assayed by HPLC in 15 minutes with a compendium assay El análisis químico (HPLC) puede detectar todos fármacos de baja calidad
    3. 3. • Has any person at your table ever found a falsified medication? What type of medicine and how did they find it? • Hay alguna de las personas en su mesa que se ha encontrado un medicamento falsificado ? ¿Qué tipo de medicamento y cómo se encuentran ?
    4. 4. if you see a this…there must be a that si usted ve un “esta” ... debe haber una “que” Interpol 2014 low risk for manufacturers profit attracts criminal enterprise Kenya 2013 Poor regulatory infrastructure lack of testing capacity Uganda 2013 supply chain problems
    5. 5. constraints and prior solutions Shimadzu HPLC at Moi U Broken for past 5 years sitting unused in a corner of Ghanaian FDA broken instruments no trained staff no supply chain Trying to buy glassware in Eldoret, Kenya Constraints for field tests: fast easy cheap no power no instruments no lab equipment no handling of chemicals results available/archived limitaciones de las pruebas: rápido fácil barato ninguna energía no hay instrumentos sin equipo de laboratorio sin manipulación de productos químicos resultados disponibles / archivado
    6. 6. Analytical challenges Many medicines have similar structures 90-120% 343 WHO Essential Medicines Dosage forms are mixtures (insoluble excipients, fixed-dose combinations) Substandard formulations are common Pills and tablets are solids
    7. 7. Wax printing • hydrophobic wax defines channels • reagents are stored in paper • passive pumping through capillary action • filter, mix, or separate based on channel geometry Yagoda, H. Ind. Eng. Chem. 1937, 9, 79–82; Carrilho, Martinez, and Whitesides, Anal. Chem. 2009, 81, 7091–7095 El papel se imprime con tinta cerosa y después se calentó para fundir la cera. Es una forma barata de hacer que los dispositivos de microfluidos.
    8. 8. Paper analytical device (PAD)
    9. 9. These color tests can identify drugs that have very similar structures Estas pruebas de color pueden identificar fármacos que tienen estructuras muy similares i. Beta lactam + amide (basic copper test) ii. Primary amine (ninhydrin) iii. Phenol (diazo coupling)
    10. 10. Ampicillin Amoxicillin Benzyl Penicillin Unknown/desconocido Reading the color bar code Cómo leer el código de barras de color Weaver et al. Analytical Chemistry, 2013, 85 (13), pp 6453–6460 Amox Amox Compare each lane to known standard Comparar cada carril de ejemplo conocido
    11. 11. How many drugs can the PAD detect? ¿Cuántos medicamentos puede detectar el PAD? 12 60 drugs give distinctive bar codes Analgesics: Acetaminophen Antipyrine Aspirin Dipyronea Gabapentene Naproxen Oxycodoneb Tramadolc Antibiotics: Amoxicillin Ampicillin Azithromycin Cephalexin Cefdinir Ciprofloxacin Doxycycline Erythromycinc Levofloxacin Minocycline Nitrofurantoin Oxytetracycline Penicillin G Streptomycin Antihistamines: Cetirizine Chlorpheniraminea Diphenhydramine Doxylamine succinate R-phenylephrine Antimalarials: Amodiaquine Lumifantrine Pyrimethaminec Quininec Chloroquine Malanil Mefloquinec Primaquine PDE-5 inhibitors Tadalafil Sildenafilc Vardenafil Diet drugs/stimulants: Caffeine Sibutraminec Phenterminec Phenolphthalein Excipients: Starch, Talc Carbonate (chalk, calcite, baking soda) Polyethylene glycolc TB medications: Ethambutol Isoniazid Pyrazinamide Rifampicin IE, RIP(E) Misc. Albendazolec Dextromethorphanb Tamiflud Metformin Evista, Lyrica ferrous sulphate hydroxyzine pamoate Sevelamer Telmisartan Heroin Crack cocaine Methamphetamine
    12. 12. What’s in a falsified medication? Active Pharmaceutical Ingredient (API) + → PharmaceuticalExcipient(s) • Adulterants/fillers • chalk • maize meal • gypsum • talcum powder • No API • Incorrect dose API • Wrong API • Degraded API
    13. 13. Please form 12 teams For each team: Para cado equipo: • 6 PADs • pure isoniazid (INH) • 4 “unknown” samples • wood sticks (palitos) • small water dishes (platillos para el agua)
    14. 14. 1) Run pure INH and pure water 2) Run 4 unknowns Demo Test card containing preloaded dry reagents Tarjeta de ensayo que contiene precargado reactivos secos Step 1: Crush tablet and apply to card You should see powder in each lane Paso 1 : se aplica el polvo a la tarjeta Usted debe ver el polvo en cada carril Step 2: Dip card in water for 3 minutes Paso 2 : Coloque el borde inferior de la tarjeta en agua durante 3 minutos Colors develop in 1-3 minutes colores aparecen en 1-3 minutos sample name sample name sample name sample name Use wooden stick to wipe powder firmly across the paper Isoniazid (INH) Instrucciones de uso del PAD
    15. 15. B, G, H: Orange-red (rojo anaranjado C, F, I: Green (verde) J: Starch gives black color that does not move El almidón da color negro que no se mueve * Does the sample contain INH? (Hay alguna INH?) * Does the sample contain anything that shouldn’t be there? (¿Hay algo sospechoso?)
    16. 16. B, G, H: Orange-red (rojo anaranjado C, F, I: Green (verde) J: Starch gives black color that does not move El almidón da color negro que no se mueve * Does the sample contain INH? (Hay alguna INH?) * Does the sample contain anything that shouldn’t be there? (¿Hay algo sospechoso?) 1 INH 30% Starch 70% 2 Antipyrine 50% Rice Flour 50% 3 Paracetamol 4 INH
    17. 17. B, G, H: Orange-red (rojo anaranjado C, F, I: Green (verde) J: Starch gives black color that does not move El almidón da color negro que no se mueve * Does the sample contain INH? (Hay alguna INH?) * Does the sample contain anything that shouldn’t be there? (¿Hay algo sospechoso?) 1 INH 30% Starch 70% 2 Antipyrine 50% Rice Flour 50% 3 Paracetamol 4 INH
    18. 18. Sensitivity: if it’s there, do you see it? Ideal = 100% Sensibilidad: si está allí , ¿lo ves? Ideal = 100 % 0 10 20 30 40 50 60 70 80 90 100 e- rich phenols Starch Talc Tertiary amines Baking Soda Chalk (med-heavy) Acetaminophen Pyrazinamide Isoniazid Rifamicin Ethambutol Ampicillin Amoxicillin Beta-lactam Specificity: if it’s not there, do you not see it? Ideal = 100% Especificidad: si no está ahí , ¿no lo ves ? Every pure API detected with 92-100% sensitivity and 88-100% specificity but reading by eye requires expert readers Pero esto requiere lectores expertos the logistics won’t work at scale
    19. 19. What the user does not see 2 3 5. Geo-tracking4. Image Analysis Time GoodSamples 6. Archiving and Monitoring Data collection with cell phones 1
    20. 20. Image analysis goals: • Classification: Assign test images to correct class based on stored “training” images Clasificación : Asigne imágenes de prueba para corregir clase basada en imágenes de "entrenamiento “ • Quantification: Measure color ratios or intensities Cuantificación : Mida las proporciones de color o intensidad • Adaptive learning: Search for patterns in data sets Aprendizaje adaptativo : Búsqueda de patrones en conjuntos de datos
    21. 21. Fixing bad pictures/corregir malas fotos “wild type” image rotation, keystoning, shadows, color distortion Image re-sized, aligned, lanes identified Prof. Pat Flynn and Prof. Chris Sweet
    22. 22. Color bar code Computer image analysis Compare unknowns to stored “authentic” bar codes using neural network. Asigne imágenes de prueba para corregir clase basada en imágenes de "entrenamiento “ • trained on 20 samples each of Acetaminophen, Acetylsalicylic Acid, Amodiaquine, Amoxicillin, Ampicillin, Artesunate, Calcium Carbonate, Corn Starch, Diethylcarbamazine, Ethambutol, Isoniazid, Rifampicin, Tetracycline • Tested with 10 samples each of same drugs (N=130) Sandipan Banerjee and Chris Sweet
    23. 23. Computer can classify accurately Counterfeit Drug Detection with Paper Analytical Device Images using Deep Learning; S. Banerjee, J. Sweet, C. Sweet, WACV 2016 submitted Acetaminophen Aspirin Amodiaquine Amoxycillin Ampicillin Artesunate CaCO3 Corn Starch DEC Ethambutol Isoniazid Rifampicin Tetracycline Acetaminophen5/10 Aspirin10/10 Amodiaquine10/10 Amoxycillin10/10 Ampicillin3/10 Artesunate3/10 CaCO36/10 CornStarch9/10 DEC10/10 Ethambutol10/10 Isoniazid10/10 Rifampicin10/10 Tetracycline10/10 Actual active ingredient (and number classified correctly) Howsurewastheneuralnetwork?
    24. 24. Color tests can detect “fillers” Pruebas de color se pueden detectar sólidos insolubles • talc eosin red dye  cherry • starch, flour I2  blue/black • chalk, baking soda, calcite Fe(III)  Fe2O3 Weaver et al. Analytical Chemistry, 2013, 85 (13), 6453–6460
    25. 25. Pharmaceuticals in “herbal” medicines Productos farmacéuticos en medicamentos “a base de hierbas" Samples from Israeli Ministry of Health— Division of Enforcement and Inspection “Ingredients:Mulberry leaf extracts, jobstears seed, medical amylum”
    26. 26. A B C D E F G H I J K L Phenolphthalein laxative use was banned in 1999 Samples from Israeli Ministry of Health— Division of Enforcement and Inspection Sibutramine anorexiant banned in 2010 “herbal” medicine medicamentos “a base de hierbas"
    27. 27. Quantification is hard • API range 90%-120% “meets standard” 50 70 90 110 130 150 170 190 100% Chloroquine (CQ) 70% CQ, 30% chalk 40% CQ, 60% starch 0% CQ, 100% aspirin 0% CQ, 100% chalk Min Outlier Max Outlier Chloroquine (CQ) 70% CQ, 30% chalk 40% CQ, 60% starch 0% CQ, 100% aspirin 0% CQ, 100% chalk Weaver et al, AJTMH 2015 Colorintensity
    28. 28. Iodometric titration on a paper card degradation products eg RSH add known amount of I2 thiosulfate/starch on test card performs back-titration KOH 20 minAmoxicillin (este PAD cuantifica el agente de yodación de la sal)
    29. 29. Quantification of beta lactams via USP <425> degradation products eg RSH add known amount of I2 back-titration with thiosulfate/starch KOH 20 minAmoxicillin
    30. 30. degradation products eg RSH add known amount of I2 thiosulfate/starch on test card performs back-titration KOH 20 minAmoxicillin N. Myers, unpublished data 50% API 75% API 90% API 95% API 100% API Substandard antibiotics don’t react with all the iodine. The more I2 is left over, the more dots turn blue Antibióticos deficientes no reaccionan con todo el yodo . El más yodo sobra , más puntos se vuelven azules
    31. 31. Can we analyze medicines in the real world? ¿Podemos analizar los medicamentos en el mundo real?
    32. 32. Question for every table Pregunta para cada mesa • If you could test the quality of five medicines in your country, which five would you pick, and why? • Si se pudiera probar la calidad de cinco medicamentos en su pais, los cuales cinco elegirías y por qué?
    33. 33. Pharmacists at Moi Hospital and staff at Kenyan Pharmacy and Poisons Board (KPPB) chose ampicillin, amoxycillin, amoxycillin/clavulanate, ciprofloxacin, and azithromycin 401 brands in Kenya O.M.G. Very low quality missing API <50% API substitute API serious risk to patients and public health Substandard does not meet pharmacopeia standards risk of harm to patients and public health Good quality Meets pharmacopeia standards
    34. 34. Secret shoppers Compradores secretos PADAdverse reactions Reacciones adversas Confirmatory HPLC HPLC confirmatorio Kenyan Pharmacy and Poisons Board
    35. 35. User training, Eldoret, Kenya
    36. 36. PADs run in Kenya one of these things is not like the other ones una de estas cosas no es como las demás
    37. 37. Confirmatory analysis by HPLC: Amoxycillin 500 mg, clavulanate 125 mg Suspicious Normal Standards Rebecca Ryan
    38. 38. Impacts Secret shoppers buy antibiotics at pharmacies in Western Kenya Kenyan Pharmacy and Poisons Board 4 reports to KPPB 2 reports to WHO Screen medicines at MTRHAdverse drug reactions from MTRH clinics HPLC at ND 167 assays, 57 substandard of which 14 lacked an API
    39. 39. Publicity bbc.com/news/health-32938075 bbc.com/news/health-32982539 [video file is >100 MB]
    40. 40. Outreach
    41. 41. Urinary iodide PAD Nutrition AirPAD air pollutants BioPAD (with Goodson) food quality ? PAD Detect falsified medicines Iodized salt SaltPAD
    42. 42. Graduate students: Nicholas Myers, Sandipan Banerjee, James Sweet, Dr. Abigail Weaver, Jamie Luther Undergraduate students: Kate Girdhar, Margaret Berta, Esseatim Etim (Winthrop), Sarah Bliese (Hamline), Rebecca Ryan, Steven Froelich, Hannah Reiser, Kellie Radell, Eliza Herrero, Leah Koenig Collaborators: Sonak Pastakia, Rahki Kharwa, Mercy Maina, Celia Ngetich, Phelix Were, and Beatrice Jakait (AMPATH/MTRH Kenya) Chris Sweet, Center for Research Computing, UND Cheryl Fleurer and Mark Witkowski, FDA/FCC Deanna O’Donnell, Hamline U. SBPD Mickey Arieli, IMOH $$: University of Notre Dame, Global Alliance for Improved Nutrition, Lilly Endowment Bill & Melinda Gates Foundation US-AID DIV program

    Editor's Notes

  • artemisinin drugs Nobel 2015
  • Sub-saharan africa: avg. per capita total health expenditures $32 (US $6,719)
    countries that have functional MRAs 135 /218
    WHO analysis of 26 MRAs in sub-saharan Africa:
    17 countries had a functional QC laboratory, 5 of those labs had documentation of their analysis procedures.
    Only 5 countries had a systematic post market testing plan; 7 tested samples when someone complained.
    8 countries collected adverse effects reports, but they didn’t act effectively on the results.

    More pictures—sad sack pictures
  • Strengths—Everything is included; orthogonal testing methods (color tests + TLC); small scale so less chemical handling; commercially available, high penetrance/distribution; detects substitute ingredients and lack of API
    Weaknesses—not quantitative, need consumable supplies, testing one substance can take hours, high knowledge/skill level required to carry out & interpret tests, ambiguous results common
    >4,000 euros + consumable supplies, 1-2 weeks training

  • Transition: need to analyze solid dosage forms (consider the constraints of the analysis…)

    falsified formulations may include unexpected excipients or APIs…

    Qualitative analysis can reveal discrepant formulations (eg trout in the milk)

    quantitative analysis of API will show more substandards
  • about this time there was a paper by whitesides describing how to print wax on paper to make sophisticated devices for enzyme assays (DFA)
  • maybe use video instead
  • fix beta lactam

  • ----- Meeting Notes (10/16/15 13:32) -----
    780 cards (read 1-4 lanes per card)
  • 24 lane PAD

    Nothing from praziquantel, mebendazole

    simplifies logistics
  • Sustaining chemistry in the developing world will lead to monitoring over geographical areas over time.
  • AT least the neural network knows when it knows something!!

    transition: so far we have looked at detection of single, pure APIs. What about dosage forms?
  • ability to detect insoluble excipients is valuable (thoreau’s trout in the milk)
  • pharmacists at Moi Hospital and KPPB wanted to study five common medicines in Western Kenya

    I did a brand survey
    81 paracetamol,
    108 amoxicillin,
    72 amoxy-clav,
    89 ciprofloxacin,
    51 azithromycin
  • This is a question about values!

  • I did a brand survey
    81 paracetamol,
    108 amoxicillin,
    72 amoxy-clav,
    89 ciprofloxacin,
    51 azithromycin
  • Minilab—713 in use, started in 1997, 22 very low quality products showcased on their website

    458 samples from Kenya sent to US in 2014
  • vision: make cheap cards that could be used in a low resource setting by untrained people
    will produce some visual readout, it will be complicated if we want to find out complex questions, but we can get around that…
    Make use of the mobile phone network to archive/interpret data
    large scale data collection
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