You will be presented with the abstract, introduction, and discussion section from a biomedical research article. Your task is to create a summary that a layperson can understand, capturing the essence of the research article. Keep essential scientific terms, but ensure that the language remains clear and the concepts are explained in an uncomplicated manner.

Title: Parsimonious Determination of the Optimal Infectious Dose of a Pathogen for Nonhuman Primate Models}
Article:
Abstract:
The nonhuman primate( NHP) model is often the best experimental model for testing interventions designed to block infection by human pathogens, such as HIV, tuberculosis, and malaria. A physiological model may require the use of a limiting dose of the infectious agent, where only a fraction of animals become infected upon any given challenge. Determining the challenge dose of the pathogen in such experiments is critical to the success of the experiment: using too-high or too-low a challenge dose may lead to false negative results and an excessive use of animals. Here I define an optimized protocol for defining the dose of pathogen that infects 50% of the time( AID50); other challenge doses, e. g. AID80, can be easily calculated from the same data. This protocol minimizes the number of animals, as well as resources and procedures, while providing an estimate of the AID50 within 1. 5-fold of the true value.
Introduction:
The nonhuman primate( NHP) can be the best( or only) experimental model for studies of human infectious pathogens and immunological interventions. NHPs represent the preferred animal models for testing vaccines and therapeutics for HIV, tuberculosis, ebola, and malaria. However, significant ethical and economic constraints accompany such models: the most prominent includes a mandate to use as few animals as possible to achieve statistically significant and biologically meaningful results. In many NHP models, it is desirable to use a limiting infectious dose as a challenge—a dose which only infects a fraction of animals at each exposure. Such exposures are generally considered to be more physiological[1], and therefore better mimic the potential clinical impact of vaccination or therapeutic intervention. In addition, low dose repeated challenges provides significantly more statistical power to discern effects of intervention[1–3]. To date, the most-used example of this is repeated mucosal challenge with SIV or SHIV for testing vaccines[4–18], microbicides[4, 13, 19–21], or peri-exposure prophylaxes[22]. Limiting-dose challenge models require careful titration of the infectious agent: using a dose that is too high( and not physiological) may lead to a false negative effect of the intervention[16], whereas a dose that is too low will lead to insufficient infections within the time frame of the experiment to yield useful data, consequently using animals for no gain as well as wasting resources. Even a 2-fold error in the challenge dose can significantly impact the power of an experiment. The goal of the analyses presented here was to define a titration protocol that minimized animal use and procedures while estimating the AID50 to within 1. 5-fold of the true value. While much of the discussion is based on NHP( and the SIV model), the protocol is generalizable to any pathogen and any animal model. A method for estimating infectious doses was published almost 80 years ago[23], Based on this method by Reed and Muench, one would use as many as 48 animals to estimate the AID50. A seminal paper by Spouge[24] used a Bayesian framework to define a method to estimate the AID50( and its precision). However, the goal at that time was to define a minimal dose infecting all animals in an experiment( e. g., AID99) following intravenous infection( using a purely stochastic model of infection). Here I model infection based in part on Michaelis-Menten kinetics, with the goal of defining a challenge dose that reproducibly infects a fraction of animals; the statistical analysis is similar to a Logit model from Spouge[24, 25]. From this, I define a parsimonious protocol to efficiently estimate the AID50 for a pathogen; this protocol will, on average, only use 15 animals.
Discussion:
From these data, and based on the optimization criteria( in order of priority: minimum number of animals, minimum number of rounds in Phase 1, maximum accuracy on the AID50 estimate), the optimum parameter values were selected. Specifically, the number of animals per cohort( NC) should be 8( with 7 being close to optimal); the target precision( σT) should be 0. 5( with 0. 6 being close); and the maximum number of exposures per animal( EMAX) should be unlimited( with 3 being close to optimal). If any information about the likely AID50 is known( e. g., from previous experiments), then a starting challenge dose( D0) should be between 0. 1 and 1 AID50. Using as many as 8 animals per round may seem unintuitive—for example, why is NC = 4 less efficient? There are two reasons for this: First, the criterion for precision( σT < 0. 5) is heavily dependent on the total number of exposures in the experiment—thus, using four animals will usually double the number of rounds in the experiment( increasing time and resources) without reducing the eventual number of animals. Second, a four animal cohort presents a much larger risk of a obtaining an outlier outcome( due to the stochastic nature of the infections), leading to incorrect dose adjustment for the following round. Fig 4B illustrates this risk. In this titration, only 3 of 8 animals were infected despite using what turned out to be a challenge dose that was ~2-fold above the actual AID50. Based on Fig 1A, using this dose is expected to infect 68% of animals challenged once; the odds of having 3 or fewer of 8 infected is low( 8%) but not insubstantial. Were this the extent of the titration, a future( large) study would have used this or even higher challenge dose. By following the procedure specified here, this statistical anomaly was overcome. The length of time required to determine the outcome of a challenge will define the length of each round. For example, with a titration of highly pathogenic SIV, infection usually be determinable by day seven and always by day 10. Thus, each subsequent round will likely be initiated two weeks apart. In contrast, during phase 2, this step can be accelerated. For example, an animal might be infected during the first round of phase 2, and then re-challenged one week later when its infection status is still unknown. The animal would become viral positive day 10( i. e., a few days after the second challenge of round 2), indicating that it actually became infected after the first round and that the subsequent challenge should be discounted. This ambiguity at day seven, fully resolved before day 14( i. e., 3rd round), does not impact the analysis of the phase 2 data. However, such ambiguity is not appropriate for phase 1, where challenges must be spaced with sufficient delay to positively establish infection. However, other pathogens may require considerably more time between rounds in phase I. For example, in titrating a stock of SHIV 162p3, we found that some animals did not become viremic until day 14, and adopted a 3 week cycle for titrations. With mTB, infection may not be clinically evident until four weeks post-exposure, and the cycle time needs to be commensurately higher. The number of animals enrolled in phase 2 is between zero and NC( i. e., the number of uninfected animals remaining from the last round of phase 1). At this point, a fairly accurate estimate of the AID50 is available, so phase 2 will usually require no more than 3–4 rounds to infect the remaining animals. Therefore, the total time required for this titration protocol will be, on average, four rounds for phase 1 and three rounds for phase 2. In the example of titration of an SIV, where each round in phase 1 requires two weeks and each round in phase 2 requires one week, the total time for the titration should be expected to be 11 weeks. In our in vivo examples( Fig 4), titration of FL14 was finished in 8 weeks, and FL14-AK in 12 weeks. Of course, the total time will depend on the specific requirements of each pathogen and system, and the timing between rounds should be adjusted accordingly. Many variations of titration protocols were evaluated, including( i) using smaller group sizes during initial rounds of Phase 1 and larger group sizes when narrowing in on the true AID50,( ii) other modes of variable group sizes; and( iii) eliminating Phase 2. Most alternatives performed worse in by requiring more animals and/or more time and procedures. The scripts created for the simulations reported here can be modified to test any configuration of the titration protocol that might be considered for a given pathogen. It should be noted that this protocol returns an estimate of the average AID50 for a cohort of animals, and will be valid for an experimental cohort that is matched for relative infection rates. For example, this protocol could be used for titration of a virus used in intravaginal challenge where the infection risk varies by time in the menstrual cycle. The best results will be obtained by challenging both titration and experimental animals at the same point in the menstrual cycle. But as long as the challenge time points are equivalently randomized across the menstrual cycle in both the titration and experimental cohorts, the estimate of the AID50 will still be valid. Most of the modeling assumed a uniform infection rate for the viruses in the challenge stock( i. e., by setting m = 1). If a swarm of genetically distinct viruses is used, in which clonal variation in infection efficiency is present, then the result may be a mixed kinetics of infection( i. e., m < 1). Modeling even a severe example of this( m = 0. 5) resulted in the same optimal parameter values, albeit the total number of animals( and rounds) may increase( or even decrease in some cases). A somewhat different model for the infecting dose response was used by Spouge his seminal paper[24]. While the motivations and goals of that paper were different than for this paper, either model can be used to estimate the AID50( Fig 4). A difference between the Spouge model and the model used here is in the estimate of the precision of the AID50( σT), which differ by a factor of about 1. 5. Consequently, if using the convenient online calculator( select the “Logit” fit)[25], multiply the error estimate by 0. 67 to approximate σT as used here. In some experiments, it may be desirable to use a higher( e. g., AID80) or lower( e. g. AID20) challenge dose. These doses can be estimated from the following formula( assuming, based on the estimated AID50, where “x” is the desired percentage of successful infections: AIDx= AID50( 100x) −1 In summary, an optimized pathogen titration protocol is presented( Fig 2) to determine with good precision the limiting infectious dose of a pathogen, a dose which may be critical for testing interventions[16]. On average( 90% range in parentheses), the protocol will enroll 15( 12–25) animals, require 3( 2–5) phase 1 rounds, and result in an estimate of the AID50 within 40% of the true value. The protocol defined here is generalizable to any pathogen and any animal model. However, it is most critical for NHP models, where ethical considerations mandate minimization of animal use. This protocol and the analyses reported here will aid in the preparation of an Animal Study Proposal( ASP) for the purposes of defining the characteristics of an NHP-infectious agent. Finally, these analyses provide a statistical justification for animal numbers in the ASP, as required for Institutional Animal Care and Use Committee( IACUC) review and approval. Importantly, by following the method described here, investigators can minimize the number of animals and resources needed to define an infectious pathogen challenge dose in NHPs.
Exposing nonhuman primates to infectious pathogens ( such as tuberculosis , malaria , or the simian equivalent of HIV ) is an important model for testing vaccines or other interventions designed to prevent infection or disease . In fact , demonstrating efficacy in animals is often a requirement before clinical testing in humans can be started . A critical variable in such testing is the dose of the pathogen used: this dose should be similar to what humans would encounter . Using too-high a dose may overcome the intervention and mask a successful approach; using too-low a dose may not be relevant . Often , an optimal dose will lead to “successful” infections only a fraction of the times the animal is exposed . A successful intervention experiment therefore needs to use a precisely calibrated dose of the infectious agent; this calibration can only be done by exposing animals to a range of doses and measuring how often they become infected . Here I define the most parsimonious method for performing this calibration: one that uses the least number of animals and procedures . Given the large number of new pathogens being tested in animal models , adoption of such a parsimonious protocol is both economically and ethically warranted , and will thereby enable favorable review of proposed animal use numbers by Institutional Animal Care and Use Committees .