A physiological approach to renal clearance: From premature neonates to adults.

AIMS: We propose using glomerular filtration rate (GFR) as the physiological basis for distinguishing components of renal clearance. METHODS: Gentamicin, amikacin and vancomycin are thought to be predominantly excreted by the kidneys. A mixed-effects joint model of the pharmacokinetics of these drugs was developed, with a wide dispersion of weight, age and serum creatinine. A dataset created from 18 sources resulted in 27,338 drug concentrations from 9,901 patients. Body size and composition, maturation and renal function were used to describe differences in drug clearance and volume of distribution. RESULTS: This study demonstrates that GFR is a predictor of two distinct components of renal elimination clearance: (1) GFR clearance associated with normal GFR and (2) non-GFR clearance not associated with normal GFR. All three drugs had GFR clearance estimated as a drug-specific percentage of normal GFR (gentamicin 39%, amikacin 90% and vancomycin 57%). The total clearance (sum of GFR and non-GFR clearance), standardized to 70 kg total body mass, 176 cm, male, renal function 1, was 5.58 L/h (95% confidence interval [CI] 5.50-5.69) (gentamicin), 7.77 L/h (95% CI 7.26-8.19) (amikacin) and 4.70 L/h (95% CI 4.61-4.80) (vancomycin). CONCLUSIONS: GFR provides a physiological basis for renal drug elimination. It has been used to distinguish two elimination components. This physiological approach has been applied to describe clearance and volume of distribution from premature neonates to elderly adults with a wide dispersion of size, body composition and renal function. Dose individualization has been implemented using target concentration intervention.

Evaluation of the Prescribing Skills Assessment implementation, performance and medical student experience in Australia and New Zealand.

AIMS: The UK Prescribing Safety Assessment was modified for use in Australia and New Zealand (ANZ) as the Prescribing Skills Assessment (PSA). We investigated the implementation, student performance and acceptability of the ANZ PSA for final-year medical students. METHODS: This study used a mixed-method approach involving student data (n = 6440) for 2017-2019 (PSA overall score and 8 domain subscores). Data were also aggregated by medical school and included student evaluation survey results. Quantitative data were analysed using descriptive and multivariate analyses. The pass rate was established by a modified Angoff method. Thematic analyses of open-ended survey comments were conducted. RESULTS: The average pass rate was slightly higher in 2017 (89%) which used a different examination to 2018 (85%) and 2019 (86%). Little difference was identified between schools for the PSA overall performance or domain subscores. There was low intercorrelation between subscores. Most students provided positive feedback about the PSA regarding the interface and clarity of questions, but an average of 35% reported insufficient time for completion. Further, 70% on average felt unprepared by their school curricula for the PSA, which is in part explained by the low prescribing experience; 69% reported completing ≤10 prescriptions during training. CONCLUSION: The ANZ PSA was associated with high pass rates and acceptability, although student preparedness was highlighted as a concern for further investigation. We demonstrate how a collaboration of medical schools can adapt a medical education assessment resource (UK PSA) as a means for fulfilling an unmet need.

TDM is dead. Long live TCI!

Twenty years ago, target concentration intervention (TCI) was distinguished from therapeutic drug monitoring (TDM). It was proposed that TCI would bring more clinical benefit because of the precision of the approach and the ability to link TCI to principles of pharmacokinetics and pharmacodynamics to predict the dose required by an individual (1). We examine the theory and clinical trial evidence supporting the benefits of TCI over TDM and conclude that in the digital age TDM should be abandoned and replaced by TCI.

Important lack of difference in tacrolimus and mycophenolic acid pharmacokinetics between Aboriginal and Caucasian kidney transplant recipients.

AIM: To examine whether differences in tacrolimus and mycophenolic acid (MPA) pharmacokinetics contribute to the poorer kidney transplant outcomes experienced by Aboriginal Australians. METHODS: Concentration-time profiles for tacrolimus and MPA were prospectively collected from 43 kidney transplant recipients: 27 Aboriginal and 16 Caucasian. Apparent clearance (CL/F) and distribution volume (V/F) for each individual were derived from concentration-time profiles combined with population pharmacokinetic priors, with subsequent assessment for between-group difference in pharmacokinetics. In addition, population pharmacokinetic models were developed using the prospective dataset supplemented by previously developed structural models for tacrolimus and MPA. The change in NONMEM objective function was used to assess improvement in goodness of model fit. RESULTS: No differences were found between Aboriginal and Caucasian groups or empirical Bayes estimates, for CL/F or V/F of MPA or tacrolimus. However, a higher prevalence of CYP3A5 expressers (26% compared with 0%) and wider between-subject variability in tacrolimus CL/F (SD = 5.00 compared with 3.25 L/h/70 kg) were observed in the Aboriginal group, though these differences failed to reach statistical significance (p = .07 and p = .08). CONCLUSION: There were no differences in typical tacrolimus or MPA pharmacokinetics between Aboriginal and Caucasian kidney transplant recipients. This means that Bayesian dosing tools developed to optimise tacrolimus and MPA dosing in Caucasian recipients may be applied to Aboriginal recipients. In turn, this may improve drug exposure and thereby transplant outcomes in this group. Aboriginal recipients appeared to have greater between-subject variability in tacrolimus CL/F and a higher prevalence of CYP3A5 expressers, attributes that have been linked with inferior outcomes.

Wide size dispersion and use of body composition and maturation improves the reliability of allometric exponent estimates.

To evaluate study designs and the influence of dispersion of body size, body composition and maturation of clearance or reliable estimation of allometric exponents. Non-linear mixed effects modeling and parametric bootstrap were employed to assess how the study sample size, number of observations per subject, between subject variability (BSV) and dispersion of size distribution affected estimation bias and uncertainty of allometric exponents. The role of covariate model misspecification was investigated using a large data set ranging from neonates to adults. A decrease in study sample size, number of observations per subject, an increase in BSV and a decrease in dispersion of size distribution, increased the uncertainty of allometric exponent estimates. Studies conducted only in adults with drugs exhibiting normal (30%) BSV in clearance may need to include at least 1000 subjects to be able to distinguish between allometric exponents of 2/3 and 1. Nevertheless, studies including both children and adults can distinguish these exponents with only 100 subjects. A marked bias of 45% (95%CI 41-49%) in the estimate of the allometric exponent of clearance was obtained when maturation and body composition were ignored in infants. A wide dispersion of body size (e.g. infants, children and adults) is required to reliably estimate allometric exponents. Ignoring differences in body composition and maturation of clearance may bias the exponent for clearance. Therefore, pharmacometricians should avoid estimating allometric exponent parameters without suitable designs and covariate models. Instead, they are encouraged to rely on the well-developed theory and evidence that clearance and volume parameters in humans scale with theory-based exponents.

Negligible impact of birth on renal function and drug metabolism.

BACKGROUND: Transition from the intrauterine to the extrauterine environment in neonates is associated with major changes in blood flow and oxygenation with consequent increases in metabolic functions. The additional impact of birth on renal function and drug metabolism above that predicted by postmenstrual age and allometry is uncertain. Increased clearance at birth could reduce analgesic effect attributable to a lowering of plasma concentration. These elimination processes can be described using the clearance concept. METHODS: Data from four publications that investigated the time course of glomerular filtration rate and clearance of paracetamol, morphine and tramadol were reanalyzed. The effect of birth, based on postnatal age, was used in conjunction with a theory-based allometric size scaling and maturation based on postmenstrual age. RESULTS: Postnatal age had a short-term effect on the time course of clearance distinguishable from the well-known slower maturation based on postmenstrual age. While elimination might be relatively reduced by 15%-45% at birth, there is a rapid increase in elimination for 1-3 weeks after birth to be 15% greater than that predicted by postmenstrual age alone. CONCLUSION: Birth is associated with a small increase in clearance in addition to that described by postmenstrual age for common analgesic drugs cleared by glucuronide conjugation (morphine, paracetamol) or by the P450 cytochrome oxidase (tramadol) and renal systems. While the increase is of biological interest, it would not be expected to have any clinically relevant impact on renal function or drug dosing. The processes of maturation described by these models are potentially applicable to any drug elimination process.

Why standards are useful for predicting doses.

Germovsek and colleagues have recently concluded that a standard approach to modelling pharmacokinetics is not wrong and appears to be at least as useful as other ad hoc methods for describing drug concentrations. There are other advantages of this approach including learning about biology, comparing different studies, detecting errors and rationalizing dose prediction. A standard approach to size and maturation is not a panacea but provides the framework for challenging new ideas and supports a consistent method of dosing in patients of all ages.

Theory-based pharmacokinetics and pharmacodynamics of S- and R-warfarin and effects on international normalized ratio: influence of body size, composition and genotype in cardiac surgery patients.

AIMS: The aims of this study are to apply a theory-based mechanistic model to describe the pharmacokinetics (PK) and pharmacodynamics (PD) of S- and R-warfarin. METHODS: Clinical data were obtained from 264 patients. Total concentrations for S- and R-warfarin were measured by ultra-high performance liquid tandem mass spectrometry. Genotypes were measured using pyrosequencing. A sequential population PK parameter with data method was used to describe the international normalized ratio (INR) time course. Data were analyzed with NONMEM. Model evaluation was based on parameter plausibility and prediction-corrected visual predictive checks. RESULTS: Warfarin PK was described using a one-compartment model. CYP2C9 *1/*3 genotype had reduced clearance for S-warfarin, but increased clearance for R-warfarin. The in vitro parameters for the relationship between prothrombin complex activity (PCA) and INR were markedly different (A = 0.560, B = 0.386) from the theory-based values (A = 1, B = 0). There was a small difference between healthy subjects and patients. A sigmoid Emax PD model inhibiting PCA synthesis as a function of S-warfarin concentration predicted INR. Small R-warfarin effects was described by competitive antagonism of S-warfarin inhibition. Patients with VKORC1 AA and CYP4F2 CC or CT genotypes had lower C50 for S-warfarin. CONCLUSION: A theory-based PKPD model describes warfarin concentrations and clinical response. Expected PK and PD genotype effects were confirmed. The role of predicted fat free mass with theory-based allometric scaling of PK parameters was identified. R-warfarin had a minor effect compared with S-warfarin on PCA synthesis. INR is predictable from 1/PCA in vivo.

What is the best size predictor for dose in the obese child?

Lean body mass is commonly proposed for anesthesia maintenance drug dosing calculations. However, total body mass used with allometric scaling has been shown to be better for propofol in obese adults and children. Fat-free mass has also been used instead of lean body mass. Fat-free mass is essentially the same as lean body mass but excludes a small percentage of mass of lipids in cell membranes, CNS, and bone marrow. Normal fat mass is a size descriptor that partitions total body mass into fat-free mass and fat mass calculated from total body mass minus fat-free mass. The relative influence of fat mass compared with fat-free mass is described by the fraction of fat mass that makes fat equivalent to fat-free mass in terms of allometric size. This fraction (Ffat) will differ for each drug and each parameter affected by body size (eg, clearance and volume of distribution). This fraction is based on the concept of theory-based allometric size. The normal fat mass based on allometric theory and partition of body mass into fat and fat-free components provides a principle-based approach explaining size and body composition effects on pharmacokinetics of all drugs in children and in adults.

Quantitative model for the blood pressure-lowering interaction of valsartan and amlodipine.

AIMS: The objective of this study was to develop a population pharmacokinetic (PK) and pharmacodynamic (PD) model to quantitatively describe the antihypertensive effect of combined therapy with amlodipine and valsartan. METHODS: PK modelling was used with data collected from 48 healthy volunteers receiving a single dose of combined formulation of 10 mg amlodipine and 160 mg valsartan. Systolic (SBP) and diastolic blood pressure (DBP) were recorded during combined administration. SBP and DBP data for each drug alone were gathered from the literature. PKPD models of each drug and for combined administration were built with NONMEM 7.3. RESULTS: A two-compartment model with zero order absorption best described the PK data of both drugs. Amlodipine and valsartan monotherapy effects on SBP and DBP were best described by an Imax model with an effect compartment delay. Combined therapy was described using a proportional interaction term as follows: (D1  + D2 ) +ALPHA×(D1 × D2 ). D1 and D2 are the predicted drug effects of amlodipine and valsartan monotherapy respectively. ALPHA is the interaction term for combined therapy. Quantitative estimates of ALPHA were -0.171 (95% CI: -0.218, -0.143) for SBP and -0.0312 (95% CI: -0.07739, -0.00283) for DBP. These infra-additive interaction terms for both SBP and DBP were consistent with literature results for combined administration of drugs in these classes. CONCLUSION: PKPD models for SBP and DBP successfully described the time course of the antihypertensive effects of amlodipine and valsartan. An infra-additive interaction between amlodipine and valsartan when used in combined administration was confirmed and quantified.

Clinical pharmacology = disease progression + drug action.

Clinical pharmacology is concerned with understanding how to use medicines to treat disease. Pharmacokinetics and pharmacodynamics have provided powerful methodologies for describing the time course of concentration and effect in individuals and in populations. This population approach may also be applied to describing the progression of disease and the action of drugs to change disease progress. Quantitative models for symptomatic and disease-modifying effects of drugs are valuable not only for describing drugs and diseases but also for identifying criteria to distinguish between types of drug actions, with implications for regulatory decisions and long-term patient care.

Dexmedetomidine pharmacokinetics in the obese.

PURPOSE: This study aims to characterize the influence of body weight and composition on the pharmacokinetics of dexmedetomidine. METHODS: Twenty obese patients and 20 non-obese patients scheduled for elective laparoscopic surgery were given dexmedetomidine infusion schemes. Venous blood samples were taken during and after dexmedetomidine administration. Population pharmacokinetic modeling was undertaken to investigate fat free mass (FFM) and normal fat mass (NFM) as size descriptors of volumes and clearances using non-linear mixed effects modeling. NFM partitions total body weight into FFM and fat mass calculated from total body weight (TBW) minus FFM. The relative influence of fat mass compared to FFM is described by the fraction of fat mass that makes fat equivalent to FFM (Ffat). RESULTS: Theory-based allometric scaling using FFM best described weight and body composition differences in clearances and volumes A negative effect of fat mass of with an exponential parameter of -0.00541/kg (95 % CI -0.0118 to -0.00246) was estimated for clearance which indicates increased fat mass is associated with impairment of clearance. CONCLUSIONS: The use of theory-based allometry with predictions of fat free mass has been able to separate the influences of weight and body composition and indicates that size-normalized clearance of dexmedetomidine is impaired in patients who are obese.

Improved prediction of tacrolimus concentrations early after kidney transplantation using theory-based pharmacokinetic modelling.

AIMS: The aim was to develop a theory-based population pharmacokinetic model of tacrolimus in adult kidney transplant recipients and to externally evaluate this model and two previous empirical models. METHODS: Data were obtained from 242 patients with 3100 tacrolimus whole blood concentrations. External evaluation was performed by examining model predictive performance using Bayesian forecasting. RESULTS: Pharmacokinetic disposition parameters were estimated based on tacrolimus plasma concentrations, predicted from whole blood concentrations, haematocrit and literature values for tacrolimus binding to red blood cells. Disposition parameters were allometrically scaled to fat free mass. Tacrolimus whole blood clearance/bioavailability standardized to haematocrit of 45% and fat free mass of 60 kg was estimated to be 16.1 l h−1 [95% CI 12.6, 18.0 l h−1]. Tacrolimus clearance was 30% higher (95% CI 13, 46%) and bioavailability 18% lower (95% CI 2, 29%) in CYP3A5 expressers compared with non-expressers. An Emax model described decreasing tacrolimus bioavailability with increasing prednisolone dose. The theory-based model was superior to the empirical models during external evaluation displaying a median prediction error of −1.2% (95% CI −3.0, 0.1%). Based on simulation, Bayesian forecasting led to 65% (95% CI 62, 68%) of patients achieving a tacrolimus average steady-state concentration within a suggested acceptable range. CONCLUSION: A theory-based population pharmacokinetic model was superior to two empirical models for prediction of tacrolimus concentrations and seemed suitable for Bayesian prediction of tacrolimus doses early after kidney transplantation.

Importance of hematocrit for a tacrolimus target concentration strategy.

PURPOSE: To identify patient characteristics that influence tacrolimus individual dose requirement in kidney transplant recipients. METHODS: Data on forty-four 12-h pharmacokinetic profiles from 29 patients and trough concentrations in 44 patients measured during the first 70 days after transplantation (1,546 tacrolimus whole blood concentrations) were analyzed. Population pharmacokinetic modeling was performed using NONMEM 7.2®. RESULTS: Standardization of tacrolimus whole blood concentrations to a hematocrit value of 45 % improved the model fit significantly (p<0.001). Fat-free mass was the best body size metric to predict tacrolimus clearance and volume of distribution. Bioavailability was 49 % lower in expressers of cytochrome P450 3A5 (CYP3A5) than in CYP3A5 nonexpressers. Younger females (<40 years) showed a 35 % lower bioavailability than younger males. Bioavailability increased with age for both males and females towards a common value at age >55 years that was 47 % higher than the male value at age <40 years. Bioavailability was highest immediately after transplantation, decreasing steeply thereafter to reach its nadir at day 5, following which it increased during the next 55 days towards an asymptotic value that was 28 % higher than that on day 5. CONCLUSIONS: Hematocrit predicts variability in tacrolimus whole blood concentrations but is not expected to influence unbound (therapeutically active) concentrations. Fat-free mass, CYP3A5 genotype, sex, age and time after transplant influence the tacrolimus individual dose requirement. Because hematocrit is highly variable in kidney transplant patients and increases substantially after kidney transplantation, hematocrit is a key factor in the interpretation of tacrolimus whole blood concentrations.

Postoperative analgesia using diclofenac and acetaminophen in children.

BACKGROUND: Diclofenac dosing in children for analgesia is currently extrapolated from adult data. Oral diclofenac 1.0 mg·kg(-1) is recommended for children aged 1-12 years. Analgesic effect from combination diclofenac/acetaminophen is unknown. METHODS: Children (n = 151) undergoing tonsillectomy (c. 1995) were randomized to receive acetaminophen elixir 40 mg·kg(-1) before surgery and 20 mg·kg(-1) rectally at the end of surgery with diclofenac suspension 0.1 mg·kg(-1) , 0.5 mg·kg(-1) , or 2.0 mg·kg(-1) before surgery or placebo. A further 93 children were randomized to receive diclofenac 0.1 mg·kg(-1) , 0.5 mg·kg(-1) , or 2.0 mg·kg(-1) only. Postoperative pain was assessed (visual analogue score, VAS 0-10) at half-hourly intervals from waking until discharge. Data were pooled with those from a further 222 children and 30 adults. One-compartment models with first-order absorption and elimination described the pharmacokinetics of both medicines. Combined drug effects were described using a modified EMAX model with an interaction term. An interval-censored model described the hazard of study dropout. RESULTS: Analgesia onset had an equilibration half-time of 0.496 h for acetaminophen and 0.23 h for diclofenac. The maximum effect (EMAX ) was 4.9. The concentration resulting in 50% of EMAX (C50 ) was 1.23 mg·l(-1) for diclofenac and 13.3 mg·l(-1) for acetaminophen. A peak placebo effect of 6.8 occurred at 4 h. Drug effects were additive. The hazard of dropping out was related to pain (hazard ratio of 1.35 per unit change in pain). Diclofenac 1.0 mg·kg(-1) with acetaminophen 15 mg·kg(-1) achieves equivalent analgesia to acetaminophen 30 mg·kg(-1) . CONCLUSIONS: Combination therapy can be used to achieve similar analgesia with lower doses of both drugs.

A pharmacokinetic framework describing antibiotic adsorption to cardiopulmonary bypass devices.

Cardiopulmonary bypass (CPB) can alter pharmacokinetic (PK) parameters and the drug may adsorb to the CPB device, altering exposure. Cefazolin is a beta-lactam antibiotic used for antimicrobial prophylaxis during cardiac surgery supported by CPB. Adsorption of cefazolin could result in therapeutic failure. An ex vivo study was undertaken using CPB devices primed and then dosed with cefazolin and samples were obtained over 1 hour of recirculation. Twelve experimental runs were conducted using different CPB device sizes (neonate, infant, child, and adult), device coatings (Xcoating™, Rheoparin®, PH.I.S.I.O), and priming solutions. The time course of saturable binding, using Bmax (binding capacity), Kd (dissociation constant), and T2off (half-time of dissociation), described cefazolin adsorption. Bmax estimates for the device sizes were neonate 40.0 mg (95% CI 24.3, 67.4), infant 48.6 mg (95% CI 5.97, 80.2), child 77.8 mg (95% CI 54.9, 103), and adult 196 mg (95% CI 191, 199). The Xcoating™ Kd estimate was 139 mg/L (95% CI 27.0, 283) and the T2off estimate was 98.4 min (95% CI 66.8, 129). The Rheoparin® and PH.I.S.I.O coatings had similar binding parameters with Kd and T2off estimates of 0.169 mg/L (95% CI 0.01, 1.99) and 4.94 min (95% CI 0.17, 59.4). The Bmax was small (< 10%) relative to a typical total patient dose during cardiac surgery supported by CPB. A dose adjustment for cefazolin based solely on drug adsorption is not required. This framework could be extended to other PK studies involving CPB.

A Randomized Trial Comparing Standard of Care to Bayesian Warfarin Dose Individualization.

The quality of warfarin treatment may be improved if management is guided by the use of models based upon pharmacokinetic-pharmacodynamic theory. A prospective, two-armed, single-blind, randomized controlled trial compared management aided by a web-based dose calculator (NextDose) with standard clinical care. Participants were 240 adults receiving warfarin therapy following cardiac surgery, followed up until the first outpatient appointment at least 3 months after warfarin initiation. We compared the percentage of time spent in the international normalized ratio acceptable range (%TIR) during the first 28 days following warfarin initiation, and %TIR and count of bleeding events over the entire follow-up period. Two hundred thirty-four participants were followed up to day 28 (NextDose: 116 and standard of care: 118), and 228 participants (114 per arm) were followed up to the final study visit. Median %TIR tended to be higher for participants receiving NextDose guided warfarin management during the first 28 days (63 vs. 56%, P = 0.13) and over the entire follow-up period (74 vs. 71%, P = 0.04). The hazard of clinically relevant minor bleeding events was lower for participants in the NextDose arm (hazard ratio: 0.21, P = 0.041). In NextDose, there were 89.3% of proposed doses accepted by prescribers. NextDose guided dose management in cardiac surgery patients requiring warfarin was associated with an increase in %TIR across the full follow-up period and fewer hemorrhagic events. A theory-based, pharmacologically guided approach facilitates higher quality warfarin anticoagulation. An important practical benefit is a reduced requirement for clinical experience of warfarin management.

Covariate modeling in pharmacometrics: General points for consideration.

Modeling the relationships between covariates and pharmacometric model parameters is a central feature of pharmacometric analyses. The information obtained from covariate modeling may be used for dose selection, dose individualization, or the planning of clinical studies in different population subgroups. The pharmacometric literature has amassed a diverse, complex, and evolving collection of methodologies and interpretive guidance related to covariate modeling. With the number and complexity of technologies increasing, a need for an overview of the state of the art has emerged. In this article the International Society of Pharmacometrics (ISoP) Standards and Best Practices Committee presents perspectives on best practices for planning, executing, reporting, and interpreting covariate analyses to guide pharmacometrics decision making in academic, industry, and regulatory settings.

External Evaluation of Population Pharmacokinetic Models of Vancomycin in Neonates: The transferability of published models to different clinical settings.

BACKGROUND: Vancomycin is one of the most evaluated antibiotics in neonates using modeling and simulation approaches. However no clear consensus on optimal dosing has been achieved. The objective of the present study was to perform an external evaluation of published models, in order to test their predictive performances in an independent dataset and to identify the possible study-related factors influencing the transferability of pharmacokinetic models to different clinical settings. METHOD: Published neonatal vancomycin pharmacokinetic models were screened from the literature. The predictive performance of 6 models was evaluated using an independent dataset (112 concentrations from 78 neonates). The evaluation procedures used simulation-based diagnostics (visual predictive check [VPC] and normalized prediction distribution errors [NPDE]). RESULTS: Differences in predictive performances of models for vancomycin pharmacokinetics in neonates were found. The mean of NPDE for 6 evaluated models were 1.35, -0.22, -0.36, 0.24, 0.66, 0.48, respectively. These differences were explained, at least partly, by taking into account the method used to measure serum creatinine concentrations. The adult conversion factor of 1.3 (enzymatic to Jaffé) was tested with an improvement in the VPC and NPDE, but it still need to be evaluated and validated in neonates. Differences were also identified between analytical methods for vancomycin. CONCLUSION: The importance of analytical techniques for serum creatinine concentrations and vancomycin as a predictor of vancomycin concentrations in neonates has been confirmed. Dosage individualisation of vancomycin in neonates should consider not only patients' characteristics and clinical conditions, but also the methods used to measure serum creatinine and vancomycin.

Disease progression and neuroscience.

The concepts of disease progression are discussed in the context of neurological disorders. The importance of understanding the time course of the response to inactive (placebo) treatment is discussed. Disease progression and response to placebo treatment both need to be considered before drug effects can be reliably identified. Criteria for distinguishing between symptomatic and disease modifying drug effects are proposed and used to interpret the results of clinical trials in pain, depression, schizophrenia, stroke, Alzheimer's disease and Parkinson's disease.