Concomitant immunity to M. tuberculosis infection

Some persistent infections provide a level of immunity that protects against reinfection with the same pathogen, a process referred to as concomitant immunity. To explore the phenomenon of concomitant immunity during Mycobacterium tuberculosis infection, we utilized HostSim, a previously published virtual host model of the immune response following Mtb infection. By simulating reinfection scenarios and comparing with data from non-human primate studies, we propose a hypothesis that the durability of a concomitant immune response against Mtb is intrinsically tied to levels of tissue resident memory T cells (Trms) during primary infection, with a secondary but important role for circulating Mtb-specific T cells. Further, we compare HostSim reinfection experiments to observational TB studies from the pre-antibiotic era to predict that the upper bound of the lifespan of resident memory T cells in human lung tissue is likely 2-3 years. To the authors' knowledge, this is the first estimate of resident memory T-cell lifespan in humans. Our findings are a first step towards demonstrating the important role of Trms in preventing disease and suggest that the induction of lung Trms is likely critical for vaccine success.

Interpretation of Non-Clinical Data for Prediction of Human Pharmacokinetic Parameters: In Vitro-In Vivo Extrapolation and Allometric Scaling

Extrapolation of pharmacokinetic (PK) parameters from in vitro or in vivo animal to human is one of the main tasks in the drug development process. Translational approaches provide evidence for go or no-go decision-making during drug discovery and the development process, and the prediction of human PKs prior to the first-in-human clinical trials. In vitro-in vivo extrapolation and allometric scaling are the choice of method for projection to human situations. Although these methods are useful tools for the estimation of PK parameters, it is a challenge to apply these methods since underlying biochemical, mathematical, physiological, and background knowledge of PKs are required. In addition, it is difficult to select an appropriate methodology depending on the data available. Therefore, this review covers the principles of PK parameters pertaining to the clearance, volume of distribution, elimination half-life, absorption rate constant, and prediction method from the original idea to recently developed models in order to introduce optimal models for the prediction of PK parameters.

Prediction of human pharmacokinetics—evaluation of methods for prediction of hepatic metabolic clearance

Methods for prediction of hepatic clearance (CLH) in man have been evaluated. A physiologically-based in-vitro to in-vivo (PB-IVIV) method with human unbound fraction in blood (fu,bl) and hepatocyte intrinsic clearance (CLint)-data has a good rationale and appears to give the best predictions (maximum ∼2-fold errors; < 25% errors for half of CL-predictions; appropriate ranking). Inclusion of an empirical scaling factor is, however, needed, and reasons include the use of cryopreserved hepatocytes with low activity, and inappropriate CLint- and fu,bl-estimation methods. Thus, an improvement of this methodology is possible and required. Neglect of fu,bl or incorporation of incubation binding does not seem appropriate. When microsome CLint-data are used with this approach, the CLH is underpredicted by 5- to 9-fold on average, and a 106-fold underprediction (attrition potential) has been observed. The poor performance could probably be related to permeation, binding and low metabolic activity. Inclusion of scaling factors and neglect of fu,bl for basic and neutral compounds improve microsome predictions. The performance is, however, still not satisfactory. Allometry incorrectly assumes that the determinants for CLH relate to body weight and overpredicts human liver blood flow rate. Consequently, allometric methods have poor predictability. Simple allometry has an average overprediction potential, > 2-fold errors for ∼1/3 of predictions, and 140-fold underprediction to 5800-fold overprediction (potential safety risk) range. In-silico methodologies are available, but these need further development. Acceptable prediction errors for compounds with low and high CLH should be ∼50 and ∼10%, respectively. In conclusion, it is recommended that PB-IVIV with human hepatocyte CLint and fu,bl is applied and improved, limits for acceptable errors are decreased, and that animal CLH-studies and allometry are avoided.

Prediction of human pharmacokinetics — renal metabolic and excretion clearance

The kidneys have the capability to both excrete and metabolise drugs. An understanding of mechanisms that determine these processes is required for the prediction of pharmacokinetics, exposures, doses and interactions of candidate drugs. This is particularly important for compounds predicted to have low or negligible non-renal clearance (CL). Clinically significant interactions in drug transport occur mostly in the kidneys. The main objective was to evaluate methods for prediction of excretion and metabolic renal CL (CLR) in humans. CLR is difficult to predict because of the involvement of bi-directional passive and active tubular transport, differences in uptake capacity, pH and residence time on luminal and blood sides of tubular cells, and limited knowledge about regional tubular residence time, permeability (Pe) and metabolic capacity. Allometry provides poor predictions of excretion CLR because of species differences in unbound fraction, urine pH and active transport. The correlation between fraction excreted unchanged in urine (fe) in humans and animals is also poor, except for compounds with high passive Pe (extensive/complete tubular reabsorption; zero/negligible fe) and/or high non-renal CL. Physiologically based in-vitro/in-vivo methods could potentially be useful for predicting CLR. Filtration could easily be predicted. Prediction of tubular secretion CL requires an in-vitro transport model and establishment of an in-vitro/in-vivo relationship, and does not appear to have been attempted. The relationship between passive Pe and tubular fraction reabsorbed (freabs) for compounds with and without apparent secretion has recently been established and useful equations and limits for prediction were developed. The suggestion that reabsorption has a lipophilicity cut-off does not seem to hold. Instead, compounds with passive Pe that is less than or equal to that of atenolol are expected to have negligible passive freabs. Compounds with passive Pe that is equal to or higher than that of carbamazepine are expected to have complete freabs. For compounds with intermediate Pe the relationship is irregular and freabs is difficult to predict. Tubular cells are comparably impermeable (for passive diffusion), and show regional differences in enzymatic and transporter activities. This limits the usefulness of microsome data and makes microsome-based predictions of metabolic CLR questionable. Renal concentrations and activities of CYP450s are comparably low, suggesting that CYP450 substrates have negligible metabolic CLR. The metabolic CLR of high-Pe UDP-glucuronyltransferase substrates could contribute to the total CL.

Prediction of human pharmacokinetics from animal data and molecular structural parameters using multivariate regression analysis: oral clearance

The aim of the study reported here was to develop a regression equation for predicting oral clearance of various kinds of drugs in humans using experimental data from rats and dogs and molecular structural parameters. The data concerning the oral clearance of 87 drugs from rats, dogs, and humans were obtained from literature. The compounds have various structures, pharmacological activities, and pharmacokinetic characteristics. In addition, the molecular weight, calculated partition coefficient (c log P), and the number of hydrogen bond acceptors were used as possible descriptors related to oral clearance in human. Multivariate regression analyses, multiple linear regression analysis, and the partial least squares (PLS) method were used to predict oral clearance in human, and the predictive performances of these techniques were compared by allometric approaches, which have been used in interspecies scaling. Interaction terms were also introduced into the regression analysis to evaluate the nonlinear relationship. For the data set used in this study, the PLS model with the tertiary term descriptors gave the best predictive performance, and the value of the squared cross-validated correlation coefficient (q(2)) was 0.694. This PLS model, using animal oral clearance data for only two species and easily calculated molecular structural parameters, can generally predict oral clearance in human better than the allometric approaches. In addition, the molecular structural parameters and the interaction term descriptors were useful for predicting oral clearance in human by PLS. Another advantage of this PLS model is that it can be applied to drugs with various characteristics.

A COMPREHENSIVE QUANTITATIVE AND QUALITATIVE EVALUATION OF EXTRAPOLATION OF INTRAVENOUS PHARMACOKINETIC PARAMETERS FROM RAT, DOG, AND MONKEY TO HUMANS. I. CLEARANCE

This study was conducted to comprehensively survey the available literature on intravenous pharmacokinetic parameters in the rat, dog, monkey, and human, and to compare common methods for extrapolation of clearance, to identify the most appropriate species to use in pharmacokinetic lead optimization, and to ascertain whether adequate prospective measures of predictive success are currently available. One hundred three nonpeptide xenobiotics were identified with intravenous pharmacokinetic data in rat, dog, monkey, and human; both body weight- and hepatic blood flow-based methods were used for scaling of clearance. Allometric scaling approaches, particularly those using data from only two of the preclinical species, were less successful at predicting human clearance than methods based on clearance as a set fraction of liver blood flow from an individual species. Furthermore, commonly used prospective measures of allometric scaling success, including correlation coefficient and allometric exponent, failed to discriminate between successful and failed allometric predictions. In all instances, the monkey tended to provide the most qualitatively and quantitatively accurate predictions of human clearance and also afforded the least biased predictions compared with other species. Additionally, the availability of data from both common nonrodent species (dog and monkey) did not ensure enhanced predictive quality compared with having only monkey data. The observations in this investigation have major implications for pharmacokinetic lead optimization and for prediction of human clearance from in vivo preclinical data and support the continued use of nonhuman primates in preclinical pharmacokinetics.

Prediction of human clearance from animal data and molecular structural parameters using multivariate regression analysis

The aim of the study reported here was to develop a method for predicting human clearance that can be applied to various kinds of drugs using clearance values for rats and dogs and some molecular structural parameters. The clearance data for rats, dogs, and humans of 68 drugs were obtained from literature. The compounds have various structures, pharmacological activities, and pharmacokinetic characteristics. In addition, molecular weight, c log P, and the number of hydrogen bond acceptors were used as possible descriptors related to the human clearance value for each drug. Three types of regression methods, multiple linear regression (MLR) analysis, partial least squares (PLS) method, and artificial neural network (ANN), were used to predict human clearance, and their predictive performances were compared with allometric approaches, which have been widely used in interspecies scaling. In MLR and PLS analyses, interaction terms were introduced to evaluate the nonlinear relationships. For the data sets used in the present study, MLR and PLS with quadratic terms gave the same equation and the best predictive performance. The value of the squared cross-validated correlation coefficient (q(2)) was 0.682. In conclusion, the MLR method using animal clearance data from only two species and using easily calculated structural parameters can generally predict human clearance better than allometric methods. This approach can be applied to drugs with various characteristics.

Prediction of hepatic metabolic clearance: comparison and assessment of prediction models

OBJECTIVE

To perform a comparative quantitative evaluation of the prediction accuracy for human hepatic metabolic clearance of 5 different mathematical models: allometric scaling (multiple species and rat only), physiologically based direct scaling, empirical in vitro-in vivo correlation, and supervised artificial neural networks.

METHODS

The mathematical prediction models were implemented with a publicly available dataset of 22 extensively metabolised compounds and compared for their prediction accuracy using 3 quality indicators: prediction error sum of squares (PRESS), r2 and the fold-error.

RESULTS

Approaches such as physiologically based direct scaling, empirical in vitro-in vivo correlation and artificial neural networks, which are based on in vitro data only, yielded an average fold-error ranging from 1.64 to 2.03 and r2 values greater than 0.77, as opposed to r2 values smaller than 0.44 when using allometric scaling combining in vivo and in vitro preclinical data. The percentage of successful predictions (less than 2-fold error) ranged from 55% (rat allometric scaling) to between 64 and 68% with the other approaches.

CONCLUSIONS

On the basis of a diverse set of 22 metabolised drug molecules, these studies showed that the most cost-effective and accurate approaches, such as physiologically based direct scaling and empirical in vitro-in vivo correlation, are based on in vitro data alone. Inclusion of in vivo preclinical data did not significantly improve prediction accuracy; the prediction accuracy of the allometric approaches was at the lower end of all methods compared.

Prospective allometric scaling: does the emperor have clothes?

We are not suggesting that an allometric relationship between pharmacokinetic parameters in animals and humans does not exist; we believe that it does. We are suggesting that using prospective AS to select doses in an FTIM study may lead to a false sense of security given the large publication bias in the literature. There are a number of unrecognized pitfalls to this approach, including (1) prediction intervals so wide as to be of limited use, (2) prediction error is often no better than arbitrarily chosen constants, and (3) it is not possible to determine which drugs will fail a priori. We encourage journals to publish studies in which prospective AS has failed so as scientists we may begin to see what makes these compounds different, with a goal toward better prediction of human pharmacokinetics.

Allometric issues in drug development

The concept of correlating pharmacokinetic parameters with body weight from different animal species has become a useful tool in drug development. The allometric approach is based on the power function, where the body weight of the species is plotted against the pharmacokinetic parameter(s) of interest. Clearance, volume of distribution, and elimination half-life are the three most frequently extrapolated pharmacokinetic parameters. Over the years, many approaches have been suggested to improve the prediction of these pharmacokinetic parameters in humans from animal data. A literature review indicates that there are different degrees of success with different methods for different drugs. Overall, though interspecies scaling requires refinement and better understanding, the approach has lot of potential during the drug development process.

Advantages of toxicokinetics in new drug development

Toxicokinetic (TK) study is generally required for toxicological evaluation and safety assessment, particularly in the pivotal toxicological studies which are requested on the basis of drug registration. The estimation points of TK data are: (1) determination of TK profile in toxicity study, (2) selection of dose, dosing form, alternative dosing route, (3) help for the evaluation of toxicity (non-effect/toxic dose, toxicological mechanism), (4) comparative evaluation between animal and human cases, (5) recommendation of the starting dose in the first human clinical trial. On the other hand, as a very recent trend, TK data are practically used for the purpose of drug discovery such as lead-optimization and candidate-selection. This paper presents practical examples of TK data in terms of their usefulness, and discusses how to evaluate the toxicity of a drug with the aid of a variety of TK studies.

Integration of in vitro data into allometric scaling to predict hepatic metabolic clearance in man: application to 10 extensively metabolized drugs

In this study, we investigated rational and reliable methods of using animal data to predict in humans the clearance of drugs which are mainly eliminated through hepatic metabolism. For 10 extensively metabolized compounds, adjusting the in vivo clearance in the different animal species for the relative rates of metabolism in vitro dramatically improved the predictions of human clearance compared to the approach in which clearance is directly extrapolated using body weight. Using hepatocyte data to normalize the in vivo clearances led to lower median deviations between the observed and predicted clearances in man compared to the approach normalizing data with brain weight (30-40% vs 60-80%, respectively). In addition, the approach integrating in vitro data appeared to be superior with respect to the range of deviations: approximately 2-fold underestimation, in the worst case, was observed by using in vitro data, whereas normalizing data by brain weight led to up to 10-fold underestimation of clearance in man. In addition, the integration of in vitro data provides a more rational basis to predict the metabolic clearance in man and may be applicable to compounds undergoing phase I and phase II metabolism as well.

The use of toxicokinetics for the safety assessment of drugs acting in the brain

Pharmacological and toxicological studies undertaken on drugs that affect the brain are frequently performed in disparate species under various experimental conditions, at doses often greatly in excess of those expected to be administered to humans, and the findings are extrapolated implicitly or explicitly with scant regard to differences in the biodisposition of the drugs. Such considerations are necessary since: 1. Species; 2. Strain; 3. Gender; 4. Route; 5. Dose; 6. Frequency and time of administration; 7. Temperature; 8. Coadministration of drugs; and 9. Surgical manipulation are but some of the factors that have been shown to influence the kinetics and metabolism of drugs. This article, using MDMA and other phenylethylamines as examples, provides evidence for the need to measure the exposure of the drugs and their active metabolites in blood and brain (toxicokinetics) in order that conclusions based only on dynamic, biochemical, or histological evidence are more pertinent. Further, the combined use of toxicokinetic-dynamic modeling can lead to a better appreciation of the mechanisms involved and a more useful approach to the calculation of safety margins.