Heijne, Janneke C. M.

Kretzschmar, Mirjam

Low, Nicola

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References1. Dooley KE, Flexner C, Andrade AS. Drug in-teractions involving combination antiretro-viral therapy and other anti-infective agents:repercussions for resource-limited countries.J Infect Dis 2008; 198:948 – 61.2. Benet LZ, Cummins CL, Wu CY. Unmaskingthe dynamic interplay between efflux trans-porters and metabolic enzymes. Int J Pharm2004; 277:3–9.3. Dixit V, Hariprasad N, Li F, Desai P, Thum-mel KE, Unadkat JD. Cytochrome P450 en-zymes and transporters induced by anti-human immunodeficiency virus proteaseinhibitors in human hepatocytes: implica-tions for predicting clinical drug interactions.Drug Metab Dispos 2007; 35:1853–9.4. Lau YY, Huang Y, Frassetto L, Benet LZ. Effectof OATP1B transporter inhibition on the phar-macokinetics of atorvastatin in healthy volun-teers. Clin Pharmacol Ther 2007; 81:194–204.5. Treiber A, Schneiter R, Häusler S, Stieger B.Bosentan is a substrate of human OATP1B1and OATP1B3: inhibition of hepatic uptakeas the common mechanism of its interactionswith cyclosporin A, rifampicin, and silde-nafil. Drug Metab Dispos 2007; 35:1400 –7.6. Weiss M, Hung DY, Poenicke K, Roberts MS.Kinetic analysis of saturable hepatic uptake ofdigoxin and its inhibition by rifampicin. EurJ Pharm Sci 2008; 34:345–50.7. Burgess G, Hoogkamer H, Collings L, Dinger-manse J. Mutual pharmacokinetic interactionsbetween steady-state bosentan and sildenafil.Eur J Clin Pharmacol 2008; 64:43–50.8. Oswald S, Giessmann T, Luetjohann D. Dispo-sition and sterol-lowering effect of ezetimibeare influenced by single-dose coadministrationof rifampin, an inhibitor of multidrug trans-port proteins. Clin Pharmacol Ther 2006; 80:477–85.9. Naesens M, Kuypers DR, Streit F, et al. Rifam-pin induces alterations in mycophenolic acidglucuronidation and elimination: implicationsfor drug exposure in renal allograft recipients.Clin Pharmacol Ther 2006; 80:509–21.10. Hirani VN, Raucy JL, Lasker JM. Conversion ofthe HIV protease inhibitor nelfinavir to a bio-active metabolite by human liver CYP2C19.Drug Metab Dispos 2004; 32:1462–7.Potential conflicts of interest: none reported.Financial support: none reported.Reprints or correspondence: Dr. Nuggehally Srinivas, Sura-mus Biopharm, 77, 10th Cross, 29th Main, J.P. Nagar I Phase,Bangalore 560 078, India (srini.suramus@yahoo.com).The Journal of Infectious Diseases 2009; 199:766–7© 2009 by the Infectious Diseases Society of America. Allrights reserved. 0022-1899/2009/19905-0023$15.00DOI: 10.1086/596746Reply to SrinivasTo theEditor—We thank Dr. Srinivas [1]for his interest in our recent review article[2] and appreciate the opportunity to re-spond to his thoughtful comments.We agree that interactions between ri-fampin and other medications, includingantiretrovirals, can be clinically or phar-macologically unpredictable and thatclose monitoring of patients taking rifam-pin with other medications is essential [3,4]. Srinivas brings to light an interestingmechanism—inhibition of hepatic up-take via blockage of organic anion trans-port— by which rifampin may causeacute paradoxical drug interactions. Hefurther reminds us that the complexity ofdrug interactions with rifampin is com-pounded when the companion drug’smetabolite is pharmacologically active, asis the case with nelfinavir. It is fortunate,however, that the hepatic transporterblockade drug-drug interaction mecha-nism he describes appears to be rare, af-fecting only a few drugs, most of whichare not commonly used in resource-limited settings. It is also fair to say thatthis type of interaction is unlikely to occurwith antiretrovirals, the vast majority ofwhich have been evaluated in drug-druginteraction studies with rifampin. In ad-dition, given that the interaction he de-scribes results in higher plasma concen-trations of a coadministered drug, thepotential impact on antiretrovirals maynot be deleterious.The comments by Srinivas are timelyand serve as a much-needed reminderthat mechanisms for drug interactions areincreasingly complex and that cliniciansneed to maintain vigilance whenever add-ing drugs to an existing regimen.Kelly E. Dooley,1,2 Charles Flexner,2 andAdriana S. Andrade1Divisions of 1Infectious Diseases and 2ClinicalPharmacology, Department of Medicine, JohnsHopkins University, Baltimore, MarylandReferences1. Srinivas NR. Unsuspected and paradoxical po-tential for drug interaction by rifampin: thingsto ponder with antiretroviral therapy [letter].J Infect Dis 2009; 199:766 –7 (in this issue).2. Dooley KE, Flexner C, Andrade AS. Drug in-teractions involving combination antiretrovi-ral therapy and other anti-infective agents: re-percussions for resource-limited countries.J Infect Dis 2008; 198:948 – 61.3. Grange S, Schutz M, Schmitt C, Riek M,Gaudeul-Ehrhart E. Unexpected hepatotoxic-ity observed in a healthy volunteer study onthe effects of multiple dose rifampicin on thesteady-state pharmacokinetics of ritonavir-boosted saquinavir and vice versa [abstract35]. In: Program and abstracts of the 6th Inter-national Workshop on Clinical Pharmacologyof HIV Therapy (Quebec, Canada), 28 –30April 2005.4. Rolla VC, da Silva Vieira MA, Pereira Pinto D,et al. Safety, efficacy and pharmacokinetics ofritonavir 400 mg/saquinavir 400 mg twicedaily plus rifampicin combined therapy inHIV patients with tuberculosis. Clin Drug In-vestig 2006; 26:469 –79.Potential conflicts of interest: A.S.A. has served on thescientific advisory board of Abbott Laboratories.Financial support: none reported.Reprints or correspondence: Dr. Adriana S. Andrade, JohnsHopkins University, 1830 E. Monument St., Ste. 8074, Balti-more, MD 21205 (aandrade@jhmi.edu).The Journal of Infectious Diseases 2009; 199:767© 2009 by the Infectious Diseases Society of America. Allrights reserved. 0022-1899/2009/19905-0024$15.00DOI: 10.1086/596747Use of MathematicalModeling to InformChlamydia Screening PolicyDecisionsTo the Editor—Regan et al. [1] predictthat Chlamydia trachomatis prevalence inwomen in Australia will fall by 70% in10 years with a screening program thattests 30% of 15–24-year-olds each year.This means that 70% of the target popu-lation would remain untested every yearand that participants would be tested, onaverage, once every 3 years. This is an op-timistic view of the impact that limitedscreening coverage would have, given theabsence of evidence that opportunistictesting at this level has controlled chla-mydia transmission up to now [2]. Wethink that there are reasons for caution inusing predictions from this model to in-form decisions on “the most effectivechlamydia screening program for Austra-lia” [1, p. 357].First, the inability to model long-termpartnerships explicitly in this compart-mental model is a fundamental limitationCORRESPONDENCE ● JID 2009:199 (1 March) ● 767that makes predictions about reductionsin prevalence unreliable. Reinfection ofthe index case patient from a current un-treated sex partner cannot be taken intoaccount in Regan et al.’s model becausethe model does not keep track of ongoingpartnerships and all infected individualsare returned to the susceptible state, aftereither treatment or natural clearance ofinfection [1]. Within an ongoing partner-ship, reinfection can take place if the part-ner is not treated along with the indexcase patient. In this model, repeat infec-tions only occur in subsequent partner-ships. In reality, early reinfection withchlamydia after screening and treatmentof an individual who has untreated part-ners is common [3]. Regan et al. assertthat “screening . . . will be the primary in-tervention in Australia” [1, p. 357]. Theyassume that their model provides a con-servative estimate of the effect of a chla-mydia screening intervention becausepartner notification would provide addi-tional benefits compared with those ofscreening alone. This can be shown to bethe case when the impact of chlamydiascreening is investigated in an individual-based model, because the net effect ofscreening without partner notificationtakes into account the reinfection of indexcase patients in partnerships in which thepartner was not screened [4]. Additionalpartner-notification efforts then have anincremental effect. Regan et al.’s com-partmental model, however, overesti-mates the effect of chlamydia screeningbecause it ignores reinfections that do notcontribute to reducing prevalence. Afterdissolution of the old partnership andformation of a new partnership, the infec-tion could be transmitted to a new sexpartner. The impact of ignoring reinfec-tion would be greater when only one sex isscreened and at low levels of coverage.Second, predicted reductions in chla-mydia prevalence cannot currently beused as a proxy for the overall effective-ness of a chlamydia screening program. Aproxy (or surrogate marker) has to have aknown relationship with the outcome [5].The primary objective of chlamydiascreening programs is to prevent compli-cations, such as tubal infertility, that re-sult from chlamydia infections that as-cend to damage the upper genital tract[2]. It is not known, however, whetherthere is a relationship between increasingscreening coverage and reducing femalereproductive tract morbidity.Regan et al.’s model is more complexthan some other compartmental modelsof C. trachomatis transmission [6] andmight be useful for examining the relativeimportance of different strategies, but itcannot be used to quantify the effects ofdifferent levels of screening coverage ac-curately. The need for empirical studiesthat demonstrate the impact of chlamydiascreening programs on both chlamydiatransmission and the incidence of com-plications remains [2]. Mathematicalmodels that dynamically incorporate theprogression to fertility-related complica-tions of chlamydia are needed to helpunderstand the impact of screening pro-grams on primary outcomes [7, 8]. Fur-ther methodological studies that directlycompare the assumptions and predic-tions of compartmental and individual-based models to help us understand howreinfection and partner notification affectthe incidence and prevalence of chla-mydia are also needed. We suggest that itis premature for health policy makers tobase decisions about chlamydia screeningprograms on this mathematical model,which does not represent the dynamics ofchlamydia transmission adequately.Nicola Low,1 Janneke C. M. Heijne,1 andMirjam Kretzschmar2,31Institute of Social and Preventive Medicine,University of Bern, Bern, Switzerland; 2Epidemiologyand Surveillance Unit, National Institute for PublicHealth and the Environment, Bilthoven, and 3JuliusCenter for Health Sciences and Primary Care,University Medical Center Utrecht, Utrecht, theNetherlandsReferences1. Regan DG, Wilson DP, Hocking JS. Coverageis the key for effective screening of Chlamydiatrachomatis in Australia. J Infect Dis 2008; 198:349 –58.2. Low N. Screening programmes for chlamydialinfection: when will we ever learn? BMJ 2007;334:725– 8.3. Scott LaMontagne D, Baster K, Emmett L, etal. Incidence and reinfection rates of genitalchlamydial infection among women aged16-24 years attending general practice, familyplanning and genitourinary medicine clinicsin England: a prospective cohort study by theChlamydia Recall Study Advisory Group. SexTransm Infect 2007; 83:292–303.4. Kretzschmar M, Welte R, van Den HA, PostmaMJ. Comparative model-based analysis ofscreening programs for Chlamydia trachoma-tis infections. Am J Epidemiol 2001; 153:90 –101.5. Fleming TR, DeMets DL. Surrogate end pointsin clinical trials: are we being misled? Ann In-tern Med 1996; 125:605–13.6. de VR, Van Bergen JE, de Jong-van den BergLT, Postma MJ. Systematic screening forChlamydia trachomatis: estimating cost-effectiveness using dynamic modeling andDutch data. PILOT-CT Study Group. ValueHealth 2006; 9:1–11.7. Low N, McCarthy A, Macleod J, et al. Epide-miological, social, diagnostic and economicevaluation of population screening for genitalchlamydial infection. Health Technol Assess2007; 11:1–165.8. Welte R, Kretzschmar M, Leidl R, van DenHA, Jager JC, Postma MJ. Cost-effectiveness ofscreening programs for Chlamydia trachoma-tis: a population-based dynamic approach. SexTransm Dis 2000; 27:518 –29.Potential conflicts of interest: none reported.Financial support: none reported.Reprints or correspondence: Dr. Nicola Low, Institute of Socialand Preventive Medicine, University of Bern, Finkenhubelweg11, CH-3012, Bern, Switzerland (low@ispm.unibe.ch).The Journal of Infectious Diseases 2009; 199:767–8© 2009 by the Infectious Diseases Society of America. Allrights reserved. 0022-1899/2009/19905-0025$15.00DOI: 10.1086/596744Reply to Low et al.To the Editor—In our article [1], we de-scribed the results from a compartmentalmodel of chlamydia transmission in a het-erosexual population. We calibrated ourmodel using the best Australian epidemi-ological and behavioral data available aswell as biological data from the literature.The stated primary aims of our work wereto compare various screening strategies interms of their effectiveness in reducingthe incidence and prevalence of chla-768 ● JID 2009:199 (1 March) ● CORRESPONDENCE

Use of Mathematical Modeling to Inform Chlamydia Screening Policy Decisions

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