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Tom kelly genetics journal club 2016

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Genetics Journal Club
To be presented at the University of Otago (Genetics Otago) on March 27th

Published in: Health & Medicine
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Tom kelly genetics journal club 2016

  1. 1. Viva la Resistance Does High-Dose Antimicrobial Chemotherapy Prevent the Evolution of Resistance? Tom Kelly – PhD candidate approx. 2 years Supervised by Mik Black & Parry Guilford (Biochemistry Dept) Genetics Journal Club 2016
  2. 2. Antibiotic Resistance Source: GuardianLV
  3. 3. Antibiotic ResistanceSource: beatricebiologist.com
  4. 4. A Rapid Evolutionary Arm’s Race Source: xkcd.com
  5. 5. A serious health issue Source: Nature Chem Bio 3:541-548 (2007) Source: emed.com.au
  6. 6. A serious health issue Source: Nature 431:892-893 (2004) Source: dfwhc Foundation Source: The Atlanic
  7. 7. A serious health issue
  8. 8. A serious (global) health issue Source: BBC Health
  9. 9. A serious (global) health issue
  10. 10. A serious (global) health issue
  11. 11. Why biologists are investigating  Novel antibiotic classes have not been discovered in some time  Multi-resistance bacteria are becoming a serious problem  We need to understand how antibiotics work and how resistance develops  Understanding non-pathogenic bacteria is also important to our health  Bacteria are an ideal system to study genes and evolution
  12. 12. Why it interests me  Mathematics + Genetics =  Bioinformatics / Computational Biology / Genomics  Rethinking conventional wisdom  Immediate clinical implications  Some neat mathematics that really matters  Similar rationale could apply to other systems, e.g., cancer
  13. 13. Day T, Read AF (2016) Does High-Dose Antimicrobial Chemotherapy Prevent the Evolution of Resistance? PLoS Comput Biol 12(1): e1004689. doi:10.1371/journal.pcbi.1004689 Published 28 January 2016
  14. 14. The “Hit Hard” Hypothesis  Ehrlich: “Hit Hard”  Fleming: “if you use penicillin, use enough”  Modern clinical advice: to administer ‘the highest tolerated antibiotic dose’  a high concentration of drug will eliminate drug-sensitive microbes quickly and thereby limit the appearance of resistant strains.  a high concentration of drug will also eliminate strains that have some partial resistance, provided the concentration is above the so-called mutant prevention concentration (MPC)
  15. 15. The “Hit Hard” Hypothesis  Ehrlich: “Hit Hard”  Fleming: “if you use penicillin, use enough”  Modern clinical advice: to administer ‘the highest tolerated antibiotic dose’  a high concentration of drug will eliminate drug-sensitive microbes quickly and thereby limit the appearance of resistant strains.  a high concentration of drug will also eliminate strains that have some partial resistance, provided the concentration is above the so-called mutant prevention concentration (MPC)  Resistant bacteria can grow above the Minimum Inhibitory Concentration (MIC)  The MPC is designed to kill all resistant single-step mutants  If the MPC is unknown clinicians are advised to give the highest possible dose
  16. 16. The “Hit Hard” Hypothesis  Ehrlich: “Hit Hard”  Fleming: “if you use penicillin, use enough”  Modern clinical advice: to administer ‘the highest tolerated antibiotic dose’  a high concentration of drug will eliminate drug-sensitive microbes quickly and thereby limit the appearance of resistant strains.  a high concentration of drug will also eliminate strains that have some partial resistance, provided the concentration is above the so-called mutant prevention concentration (MPC)  Resistant bacteria can grow above the Minimum Inhibitory Concentration (MIC)  The MPC is designed to kill all resistant single-step mutants  If the MPC is unknown clinicians are advised to give the highest possible dose  High level resistance (HLR) strains have resistance so high the drug is ineffective  Resistant to drug concentrations above those tolerable / feasible in the clinic
  17. 17. The “Hit Hard” Hypothesis Source: sciencedaily.com (press release from Penn State University, Jan 28 2016)
  18. 18. Considering Lower Doses  Does this hold up in light of evolutionary biology?  Are we not selecting for the very microbes we fear most?  Those with resistance to higher doses than safe to use in patients  Can we design drugs / dosage to reduce the risk of developing resistance in the future  May also lead to better patient outcomes  Hitting hard may work sometimes but it isn’t a good ‘rule of thumb’  Need to consider drugs on case-by-case basis based on therapeutic window  Could lead to immediate changes in existing clinical practice and new clinical trials  Could reduce the risk of adverse drug effects and allergic reactions
  19. 19. Evolutionary Processes  Competitive Suppression  Occurs at low doses of antibiotics  Wild-type has a selective advantage (due to cost of resistance)  Competitive Release / Escape  Occurs at high doses of antibiotics  Drug susceptible population removed (freeing resistant strains from competition)
  20. 20. Evolutionary Processes H (Infected)
  21. 21. Evolutionary Processes H (Infected) H (Cured) Killed by Treatment
  22. 22. Evolutionary Processes H (Infected) H (Cured) Wild-type Killed by Treatment H (Hidden) Mutation H (Hidden) H (Hidden)
  23. 23. Evolutionary Processes H (Infected) H (Cured) H (Hidden) H (Emerge) Mutation Escape / Release At high doses H (Hidden) H (Emerge) Wild-type Killed by Treatment
  24. 24. Evolutionary Processes H (Infected) H (Cured) H (Hidden) Mutation Suppression / Outcompeted At low doses H (Hidden) H (Hidden) Wild-type Killed by Treatment
  25. 25. Evolutionary Processes H (Infected) H (Cured) H (Hidden) H (Emerge) Mutation Escape / Release Suppression / Outcompeted At low doses At high doses Wild-type Killed by Treatment
  26. 26. Understanding Drug treatment  Patient treatment regimen depends on:  choice of antimicrobial drug (or drugs)  determining the frequency, timing, and duration of administration  dosage / concentration (most controversial)  Aim: to determine how the probability of resistance emergence depends on drug concentration
  27. 27. Understanding Drug treatment  Patient treatment regimen depends on:  choice of antimicrobial drug (or drugs)  determining the frequency, timing, and duration of administration  dosage / concentration (most controversial)  Aim: to determine how the probability of resistance emergence depends on drug concentration  Emergence of resistances requires occurrence of a rare mutant strain (pre-existing or de novo mutation) and it’s proliferation to clinically significant levels (in competition with the wild-type strain).
  28. 28. Understanding Drug treatment  Patient treatment regimen depends on:  choice of antimicrobial drug (or drugs)  determining the frequency, timing, and duration of administration  dosage / concentration (most controversial)  Aim: to determine how the probability of resistance emergence depends on drug concentration  Emergence of resistances requires occurrence of a rare mutant strain (pre-existing or de novo mutation) and it’s proliferation to clinically significant levels (in competition with the wild-type strain).  Concentrations limited to a within “therapeutic window” between:  Lowest dose effective against wildtype strains  Highest dose safe to use without host toxicity
  29. 29. Assumptions  Assume drug concentration is a constant ‘dose’ during treatment  Tool to understand evolution of resistance  Host factors (e.g., immune density) proliferate and act against a pathogen  Model across all theoretically possible doses, consider feasible doses within therapeutic window (lowest effective dose, highest safe dose)  Highly resistant HLR is one mutational step from wild-type  Focus on extreme resistance that cannot be treated, makes drug useless  MIC, MPC, and intermediate resistance levels ignored  HLR strain has a metabolic or replicative cost  Unable to replicate if vastly outnumbered by wildtype  Most assumptions are relaxed in supplementary (with no impact on findings)
  30. 30. Assumptions and parameters  Assume drug concentration is a constant ‘dose’ during treatment  Constant dose c  Host factors (e.g., immune density) proliferate and act against a pathogen  Pathogen (wildtype) density P(t) and Host factors X(t) over time  X defined as the inverse of immune density = How good environment for wildtype  Depend on duration of treatment and dose: p(t,c) and x(t, c)  Model across all theoretically possible doses c, consider feasible doses within therapeutic window: cϵ[cL, cU]  Highly resistant HLR is one mutational step from wild-type  Mutation to HLR occurs from wildtype population as it goes extinct at rate λ(p(t,c),c)  HLR strain has a metabolic or replicative cost  Probability of escape from competitive suppression π(x(t,c),c)
  31. 31. Assumptions and parameters  Highly resistant HLR is one mutational step from wild-type  Mutation to HLR occurs from wildtype population as it goes extinct at rate λ(p(t,c),c)  limc→ ∞ λ(p,c) = 0 (enough drug kills all wildtype, no mutation possible)  HLR strain has a metabolic or replicative cost  Probability of escape from competitive suppression π(x(t,c),c)  limc→ ∞ π(x,c) = 0 (high enough drug kills even resistant strains, even if above safe doses)  Formal definition of HLR, either:  π(x,c) ≈ π(x,0) ∀ c > cU (HLR - clinically accepted doses give more selective advantage than inhibiting growth to resistant strains)  Otherwise there is no resistance problem - strains are treatable within therapeutic window
  32. 32. Modelling Risk of Resistance Evolving
  33. 33. Rate of Risk of Resistance Evolving
  34. 34. Rate of Risk of Resistance Evolving Derivatives (initial rate of change) Derivative
  35. 35. Rate of Risk of Resistance Evolving Emergence of rare resist strains (to clinically significant levels) Depends on drug concentration
  36. 36. Rate of Risk of Resistance Evolving Change in de novo mutation (towards highly resistant)
  37. 37. Rate of Risk of Resistance Evolving Change in de novo mutation (towards highly resistant) Higher mutation in larger wildtype population (+ve) Lower mutation with higher dose against replication (-ve) Wildtype density decreases during treatment (usually -ve)
  38. 38. Rate of Risk of Resistance Evolving Change in de novo mutation (towards highly resistant)  Therefore high-dose decreases rate mutations arise during treatment  As assumed by clinicians are proponents of the “Hit Hard” Model  Unless treatment is mutagenic, or resistance conc. dependent (efflux, metabolised)
  39. 39. Rate of Risk of Resistance Evolving Replication of newly emerged highly resistant strains Change in de novo mutation (towards highly resistant)
  40. 40. Rate of Risk of Resistance Evolving Replication of newly emerged highly resistant strains Change in de novo mutation (towards highly resistant) More favourable host environment for escape (+ve) dx/dc higher dose removes wildtype aiding host (often +ve) Drug directly supresses proliferation (-ve) small in HLR
  41. 41. Rate of Risk of Resistance Evolving Replication of newly emerged highly resistant strains Change in de novo mutation (towards highly resistant)  Therefore high-dose indirectly increases replication of HLR that arise during treatment  Evolutionary processes during emergence (to clinically significant levels) need to be considered
  42. 42. Rate of Risk of Resistance Evolving Replication of newly emerged highly resistant strains Change in de novo mutation (towards highly resistant) Replication of pre-existing highly resistant strains
  43. 43. Rate of Risk of Resistance Evolving Replication of newly emerged highly resistant strains Change in de novo mutation (towards highly resistant) Replication of pre-existing highly resistant strains dx/dc higher dose removes wildtype aiding host (often +ve) Drug directly supresses proliferation (-ve) small in HLR More favourable host environment for escape (+ve)
  44. 44. Rate of Risk of Resistance Evolving Replication of newly emerged highly resistant strains Change in de novo mutation (towards highly resistant) Replication of pre-existing highly resistant strains  Therefore high-dose indirectly increases replication of HLR that existed before treatment  Evolutionary processes during emergence oppose (resistance is unfavourable at either extreme)
  45. 45. Solving An Integral – Numerical Integration  There are several ways to solve or approximate an integral (as a sum)  Risk is the area under a curve
  46. 46. Solving An Integral – Numerical Integration  There are several ways to solve or approximate an integral (as a sum)  Risk is the area under a curve Rectangle Rule Simpson’s Method Trapezium Rule
  47. 47. Solving An Integral – Numerical Integration  There are several ways to solve or approximate an integral (as a sum)  Risk is the area under a curve  Straightforward to compute, scale, and simulate on a computer Rectangle Rule Source: Khurram Wadee (CC) Wikipedia
  48. 48. Solving An Integral – Numerical Integration  There are several ways to solve or approximate an integral (as a sum)  Risk is the area under a curve  Straightforward to compute, scale, and simulate on a computer Rectangle Rule Trapezium Rule Source: Khurram Wadee (CC) Wikipedia
  49. 49. Solving An Integral – Numerical Integration  There are several ways to solve or approximate an integral (as a sum)  Risk is the area under a curve  Straightforward to compute, scale, and simulate on a computer Rectangle Rule Simpson’s MethodTrapezium Rule Source: Khurram Wadee (CC) Wikipedia
  50. 50. General Findings
  51. 51. General Findings  Intermediate doses have the highest risk of highly resistant strains  Optimal dose is either:  the largest tolerable dose  or the smallest clinically effective dose
  52. 52. General Findings  Intermediate doses have the highest risk of highly resistant strains  Optimal dose is  the largest tolerable dose  or the smallest clinically effective dose Never anything between
  53. 53. Specific Examples  Model of within-host dynamics of infection and resistance  Acute infection  Elicits immune response  Can clear infection  Treatment to reduce mortality and patient harm  Consider cases where:  1) max safe dose sufficient to cause suppression of resistant strains  2) max safe dose is not sufficient to cause suppression of resistant strains  Notice how a small difference in conditions (parameter values)
  54. 54. Specific Examples – High Dose Effective High dose more effective “Hit Hard” works (as expected)
  55. 55. Specific Examples – Low Dose Effective Lowest dose more effective at controlling resistance emergence High dose leads to rampant resistance “Hit Hard” backfires Resistant strain appears Resistant strain emerges
  56. 56. Specific Examples – Strain Outbreak
  57. 57. Specific Examples – Strain Outbreak
  58. 58. Implications – balance of opposing forces Derivatives (initial rate of change) Replication of newly emerged highly resistant strains Change in de novo mutation (towards highly resistant) Replication of pre-existing highly resistant strains
  59. 59. Evolutionary Theory for Drug Treatment  General theoretical treatment of drug treatment strategies  Opposing Evolutionary processes  Higher Energy cost – outcompeted by drug susceptible bacteria at low doses  Drug Resistance benefit – selective advantage at higher doses  Leads to a unimodal relationship between drug concentration and resistance emergence  Optimal Strategies  either the largest tolerable dose  or the smallest clinically effective dose  Combination therapy may be more effective than high dose monotherapy
  60. 60. Comparison to earlier studies  Ankomah & Levin (2014)  Used a more complex model  These considerations did not change the overall findings (supplementary)  Defined resistance evolution in terms of  1) probability of appearance (comparable to Day & Read without emergence / escape)  2) time to clear infection  Consistent with mutation appearing de novo reduced by high dose  Did not account for selective suppression while reaching clinically significant levels once a mutant strain had appeared  Predicted the case where higher doses are more effective, not where lower doses are a more suitable alternative  Higher dose reduces probability that mutations occur  However, resistant strains are also more likely to replicate to clinically significant levels at higher doses (higher competitive advantage)
  61. 61. Does the MPC Rationale work?  MPC inhibits replication of every ‘single step’ mutant  Assumes: MPC within window, no variation in dosage below MPC, no horizontal gene transfer  Finding the MPC is a waste of time, worst strategy in some cases (controversial conclusion)  Better treatment at either extreme of therapeutic window, good source of empirical evidence Good idea Better idea
  62. 62. Does the “Hit Hard” Rationale work?  Often recommended if MPC is unknown  Works in some cases but has potential to backfire (one of two possible optimal strategies)  Inherent focus on high-dose treatment in research / clinic – need to consider low doses too Better idea Counterproductive
  63. 63. Empirical Evidence for Unimodal Distribution 23.Negri MC, Morosini MI, Loza E, Baquero F. In-vitro selective antibiotic concentrations of beta-lactams for penicillin-resistant Streptococcus-pneumoniae populations. Antimicrobial Agents and Chemotherapy. 1994;38:122–125. doi: 10.1128/AAC.38.1.122. pmid:8141563 24.Firsov AA, Vostrov SN, Lubenko IY, Drlica K, Portnoy YA, Zinner SH. In vitro pharmacodynamic evaluation of the mutant selection window hypothesis using four fluoroquinolones against Staphylococcus aureus. Antimicrobial Agents and Chemotherapy. 2003;47:1604–1613. doi: 10.1128/AAC.47.5.1604- 1613.2003. pmid:12709329 25.Zinner SH, Lubenko IY, Gilbert D, Simmons K, Zhao X, Drlica K, et al. Emergence of resistant Streptococcus pneumoniae in an in vitro dynamic model the simulates moxifloxacin concentrations inside and outside the mutant selection window: related changes in susceptibility, resistance frequency and bacterial killing. Journal of Antimicrobial Chemotherapy. 2003;52:616–622. doi: 10.1093/jac/dkg401. pmid:12951352 26.Jumbe N, Louie A, Leary R, Liu WG, Deziel MR, Tam VH, et al. Application of a mathematical model to prevent in vivo amplification of antibiotic-resistant bacterial populations during therapy. Journal of Clinical Investigation. 2003;112:275–285. doi: 10.1172/JCI16814. pmid:12865415 27.Gumbo T, Louie A, Deziel MR, Parsons LM, Salfinger M, Drusano GL. Selection of a moxifloxacin dose that suppresses drug resistance in Mycobacterium tuberculosis, by use of an in vitro pharmacodynamic infection model and mathematical modeling. Journal of Infectious Diseases. 2004;190:1642–1651. doi: 10.1086/424849. pmid:15478070 28.Firsov AA, Vostrov SN, Lubenko IY, Arzamastsev AP, Portnoy YA, Zinner SH. ABT492 and levofloxacin: comparison of their pharmacodynamics and their abilities to prevent the selection of resistant Staphylococcus aureus in an in vitro model. Journal of Antimicrobial Chemotherapy. 2004;54:178–186. doi: 10.1093/jac/dkh242. pmid:15190041 29.Croisier DE, M Etienne M, Bergoin E, Charles PE, Lequeu C, Piroth L, et al. Mutant selection window in levofloxacin and moxifloxacin treatments of experimental pneumococcal pneumonia in a rabbit model of human therapy. Antimicrobial Agents and Chemotherapy. 2004;48:1699–1707. doi: 10.1128/AAC.48.5.1699- 1707.2004. pmid:15105123 30.Etienne M, Croisier D, Charles PE, Lequeu C, Piroth L, Portier H, et al. Effect of low-level resistance on subsequent enrichment of fluoroquinolone-resistant Streptococcus pneumoniae in rabbits. Journal of Infectious Diseases. 2004;190:1472–1475. doi: 10.1086/423853. pmid:15378440 31.Tam VH, Schilling AN, Neshat S, Poole K, Melnick DA, Coyle EA. Optimization of meropenem minimum concentration/MIC ratio to suppress in vitro resistance ofPseudomonas aeruginosa. Antimicrobial Agents and Chemotherapy. 2005;49:4920–4927. doi: 10.1128/AAC.49.12.4920-4927.2005. pmid:16304153 32.Tam VH, Louie A, Deziel MR, Liu W, Leary R, Drusano GL. Bacterial-population responses to drug- selective pressure: examination of Garenoxacin’s effect on Pseudomonas aeruginosa. Journal of Infectious Diseases. 2005;192:420–428. doi: 10.1086/430611. pmid:15995955 33.Firsov AA, Smirnova MV, Lubenko IY, Vostrov SN, Portnoy YA, Zinner SH. Testing the mutant selection window hypothesis with Staphylococcus aureus exposed to daptomycin and vancomycin in an in vitro model. Journal of Antimicrobial Chemotherapy. 2006;58:1185–1192. doi: 10.1093/jac/dkl387. pmid:17028094 34.Cui JC, Liu YN, Wang R, Tong WH, Drlica K, Zhao XL. The mutant selection window in rabbits infected with Staphylococcus aureus. Journal of Infectious Diseases. 2006;194:1601–1608. doi: 10.1086/508752. pmid:17083047 35.Tam VH, Louie A, Deziel MR, Liu W, Drusano GL. The relationship between quinolone exposures and resistance amplification is characterized by an inverted U: a new paradigm for optimizing pharmacodynamics to counterselect resistance. Antimicrobial Agents and Chemotherapy. 2007;51:744–747. doi: 10.1128/AAC.00334-06. pmid:17116679 36.Gumbo T, Louie A, Deziel MR, Liu WG, Parsons LM, Salfinger M, et al. Concentration- dependent Mycobacterium tuberculosis killing and prevention of resistance by rifampin. Antimicrobial Agents and Chemotherapy. 2007;51:3781–3788. doi: 10.1128/AAC.01533-06. pmid:17724157 37.Bourgeois-Nicolaos N, Massias L, Couson B, Butel MJ, Andremont A, Doucet-Populaire F. Dose dependence of emergence of resistance to linezolid in Enterococcus faecalis in vivo. Journal of Infectious Diseases. 2007;195:1480–1488. doi: 10.1086/513876. pmid:17436228 38.Goessens WHE, Mouton JW, ten Kate MT, Bijll AJ, Ott A, Bakker-Woudenberg I. Role of ceftazidime dose regimen on the selection of resistant Enterobacter cloacae in the intestinal flora of rats treated for an experimental pulmonary infection. Journal of Antimicrobial Chemotherapy. 2007;59:507–516. doi: 10.1093/jac/dkl529. pmid:17289765 39.Stearne LET, Goessens WHF, OlofssonCars JW, Gyssens IC. Effect of dosing and dosing frequency on the efficacy of ceftizoxime and the emergence of ceftizoxime resistance during the early development of murine abscesses caused by Bacteroides fragilis and Enterobacter cloacae mixed infection. Antimicrobial Agents and Chemotherapy. 2007;51:3605–3611. doi: 10.1128/AAC.01486-06. pmid:17646416 40.Zhu YL, Mei Q, Cheng J, Liu YY, Ye Y, Li JB. Testing the mutant selection window in rabbits infected with methicillin-resistant Staphylococcus aureus exposed to vancomycin. Journal of Antimicrobial Chemotherapy. 2012;67:2700–2706. doi: 10.1093/jac/dks280. pmid:22809703 41.Schmalstieg AM, Srivastava S, Belkaya S, Deshpande D, Meek C, Leff R, et al. The antibiotic resistance arrow of time: Efflux pump induction is a general first step in the evolution of Mycobacterial drug resistance. Antimicrobial Agents and Chemotherapy. 2012;56:4806–4815. doi: 10.1128/AAC.05546-11. pmid:22751536
  64. 64. Recommendations for Clinical Practice  If relative positions of hazard curve and therapeutic window are known  Rational choice of dose (to minimise risk of resistance) is possible  Choose the end of therapeutic window with lowest hazard (zero if possible)  This is well known for a range of strains and drugs (MPC/MIC experiments)  If HLR hazard curve is unknown  No need to estimate whole curve – test extreme values  Possible to compare extremes in vitro (culture) or in vivo (animal) experiments  Ethical and Practical to test in patients (clinical trials) as we’re comparing known clinically safe doses, particularly for altering use of existing / approved drugs  Could be possible to switch dosage in response to changing optimal treatment if conditions change  Potentially applicable to cancer chemotherapy  Although cancer drugs are notorious for narrow therapeutic window
  65. 65. Practical Limitations in Clinical Practice  Best resistance management is at one extreme of therapeutic window  In practice clinicians cautiously avoid these extremes (margin for error)  More aggressive than minimum effective dose  Ensures no patients fail treatment  Less aggressive than maximum tolerable dose  Ensures no patients have drug toxicity
  66. 66. Practical Limitations in Clinical Practice  Best resistance management is at one extreme of therapeutic window  In practice clinicians cautiously avoid these extremes (margin for error)  More aggressive than minimum effective dose  Ensures no patients fail treatment  Less aggressive than maximum tolerable dose  Ensures no patients have drug toxicity  Is this caution clinically justified or perceived?  “Better Safe than Sorry” … when lives are at stake  Need to consider low dose / short courses – promise some in clinical trials  Need to accurately determine the therapeutic window (esp. for new drugs)
  67. 67. Day T, Read AF (2016) Does High-Dose Antimicrobial Chemotherapy Prevent the Evolution of Resistance? PLoS Comput Biol 12(1): e1004689. doi:10.1371/journal.pcbi.1004689 Published 28 January 2016
  68. 68. Evolutionary Theory for Drug Treatment CL = Lowest Effective Dose; CU = Highest Safe Dose Probability of Evolving (Untreatable) Resistance

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