# Levodopa Slows Progression of Parkinson’s Disease. External Validation by Clinical Trial Simulation

## Authors

- First Online:

- Received:
- Accepted:

DOI: 10.1007/s11095-006-9202-3

- Cite this article as:
- Chan, P.L.S., Nutt, J.G. & Holford, N.H.G. Pharm Res (2007) 24: 791. doi:10.1007/s11095-006-9202-3

## Abstract

### Purpose

To externally validate the model predictions of a DATATOP cohort analysis through application of clinical trial simulation with the study design of the ELLDOPA trial.

### Methods

The stochastic pharmacokinetic-pharmacodynamic and disease progress model was developed from the large DATATOP cohort of patients followed for 8 years. ELLDOPA was designed to detect a difference between placebo and levodopa treated arms in the total Unified Parkinson’s Disease Rating Scale (UPDRS) taken at baseline and following 2 weeks levodopa washout after 40 weeks of treatment. The total UPDRS response was simulated with different assumptions on levodopa effect (symptomatic with/without disease modifying capability) and washout speed of symptomatic effect.

### Results

The observed results of ELLDOPA were similar to the model predictions assuming levodopa slows disease progression and has a slow washout of symptomatic effect.

### Conclusions

This simulation work confirmed the conclusion of the DATATOP analysis finding that levodopa slows disease progression. The simulation results also showed that a dose-related increased rate of progression in Parkinson’s disease, obscured by symptomatic benefit, is very unlikely. Finally, the simulation results also shown that 2 weeks washout period was not adequate to completely eliminate the symptomatic benefits of levodopa.

### Key words

clinical trial simulationDATATOPdisease progress modelELLDOPAParkinson’s diseaseprotective treatment### Abbreviations

- α
rate of natural disease progression

- BEML
symptotic maximum value of Emax

- C
_{1} levodopa concentration in the central compartment

- C
_{2} levodopa concentration in the peripheral compartment

- C5L
levodopa concentration at which 50% of Emax is produced

- Ce
levodopa concentration in the effect compartment

- Ce
_{Slow} levodopa concentration in the slow washout compartment

- CL
total body clearance

- CL
_{ic} intercompartmental clearance

- Dprog
_{PCB} natural disease progression in placebo arm

- ED50
levodopa concentration (relative to a 300 mg/d dose rate) at which 50% of Emax is produced

- Emax
maximum lowering of total UPDRS that levodopa can produce

- Emax0
emax at time 0

- E
_{O} effect of levodopa as an offset to the disease progress model

- E
_{S} effect of levodopa on the slope of the disease progress model

- FWO
fast washout of levodopa symptomatic benefits process

- KA
first-order absorption rate constant

- KLD
_{P} protective effect parameter for the rate of disease progression in relation to levodopa concentration

- KLD
_{T} toxic effect parameter for the rate of disease progression in relation to levodopa concentration

- Pmiss
probability of missing a scheduled dose having taken a dose

- PPV
population parameter variability

- Prot_Symp_SWO
_{DOSE} simulated total UPDRS in a specific dose arm assuming levodopa has both functional protective and symptomatic benefits and with a slow washout process for the symptomatic benefit after levodopa withdrawal

- Prot
_{DFP} size of protective effect (%) computed using the difference from placebo approach

- Ptake
probability of taking a scheduled dose having missed a dose

- S0
disease status at the start of the study

- SWO
slow washout of levodopa symptomatic benefits process

- Symp_SWO
_{DOSE} simulated total UPDRS in a specific dose arm assuming levodopa only has symptomatic benefit and with a slow washout process

- Symp
_{DFP} size of symptomatic effect (%) computed using the difference from placebo approach

- TEML
half-life of change in Emax

- TEQL
equilibration half-life of the equilibration effect compartment

- TEQWO
half-life of washout of levodopa symptomatic benefits

- Tlastdose
time of last levodopa dose

- Tlastobs
time of last observation

- UPDRS
unified Parkinson’s Disease Rating Scale

- V
_{1} volume of distribution of the central compartment

- V
_{2} volume of distribution of the peripheral compartment

- WT
body weight

## INTRODUCTION

The progression of motor signs of Parkinson’s disease is caused by the ongoing degeneration of dopaminergic neurons in the nigral-striatal pathway. A functionally protective treatment would slow down, halt, or even reverse (“restorative”) disease progression (1). If protective treatment is stopped the disease state will be different from that expected if no treatment had been given. On the other hand, symptomatic treatment would only reduce symptom severity during treatment. When a symptomatic treatment is stopped the disease state will return to the expected state as if no treatment had been given (2). The symptomatic benefits of levodopa have been well recognized since Cotzias *et al*. (3) showed that orally administered levodopa relieved clinical symptoms of Parkinson’s disease. Levodopa remains the most effective drug therapy in managing Parkinson’s disease. However, the effect of levodopa on natural disease progression remains unclear. Some studies suggest that levodopa protects the surviving dopaminergic cells (4–8) while others propose that levodopa accelerates dopaminergic cell death (9–12). We have developed a stochastic model for anti-parkinsonian drug response and progression in Parkinson’s disease (13) to try to identify symptomatic, protective and toxic effects of various long-term anti-parkinsonian therapies. The model was built based on the total Unified Parkinson’s Disease Rating Scale (UPDRS) scores collected in 800 parkinsonian patients enrolled in the DATATOP (Deprenyl and Tocopherol Anti-oxidative Therapy On Parkinsonism) (15) and followed for 8 years. For the placebo-levodopa subset data, the model predicts that 300 mg/d levodopa slows the rate of disease progression by 3.4 u/year (13).

A clinical trial, ELLDOPA (Earlier *vs* Later L-DOPA), has been carried out by the Parkinson Study Group with the primary objective of determining if levodopa treatment changes the progression of early Parkinson’s disease (14). ELLDOPA provided an opportunity to test our model and examine the interpretation of the results. The results of the ELLDOPA trial and of this simulation were first announced in November 2002 (16,17) and ELLDOPA was published 2 years later (18). The ELLDOPA investigators did not feel they could conclude that levodopa had a functional protective effect because of confounding with remaining symptomatic effects which had not washed out.

The ELLDOPA trial analysis and interpretation, comparing pre-levodopa treatment scores with the scores 2 weeks after withdrawal of 9 months of levodopa therapy, is dependent upon the complete washout of levodopa symptomatic effects. We therefore simulated both fast and slow washout in the model. It is impossible to separate the symptomatic and disease modifying effects of levodopa without making some assumptions about the time course of washout of symptomatic effects. Guimaraes *et al*. (19) have shown that because of the confounding effect of symptomatic benefits on disease progression, assumptions about the shape of a disease progress model in Parkinson’s disease could result in a substantial change in study design, for example, sample-size calculations.

The overall objective of this report was to externally validate the model predictions of the DATATOP cohort analysis (13). Clinical trial simulation of the study design of the ELLDOPA trial was used under different assumptions of type of treatment effects (protective or toxic) and the time course of symptomatic washout of levodopa effects.

## METHODS

### Design of ELLDOPA Trial

The experimental design, recruitment of subjects, data acquisition and statistical method has been reported in detail elsewhere (14). Briefly, ELLDOPA is a double blind, randomized, parallel, placebo controlled and multicenter (35 centers) clinical trial. Three hundred and sixty early stage Parkinsonian patients who were not receiving anti-parkinsonian medication and who were not in need of symptomatic therapy were randomized into one of the four arms (placebo, low, medium and high dose). The daily oral carbidopa/levodopa dose was titrated from 12.5/50 mg up to 37.5/150 mg (low dose), 75/300 mg (medium dose) and 150/600 mg (high dose). The ELLDOPA study was designed to detect a 4-unit difference in total UPDRS between the placebo and the high dose arms with a power of 80% (14).

The planned duration of the study was 40 weeks of levodopa treatment or placebo followed by a 2-week levodopa withdrawal period. The withdrawal period included a 3-day step down reduction of levodopa dose followed by 11 days off treatment. Subjects were not blinded to treatment withdrawal. Medications were given three times daily. All medications were taken after meals to minimize the occurrence of nausea or vomiting. Total UPDRS (range 0–188) was used to assess disease state. The higher the score, the more severe the disease.

Study Design of ELLDOPA Trial

Study Property | Description |
---|---|

Design | Double blind, parallel, randomized, placebo-controlled, multicenter (35 centers) |

Subjects | 360 subjects with early, mild Parkinsonism, who have not previously received levodopa, randomly assigned to one of four arms (90 in each arm) |

Arms, | Placebo, 0 |

Carbidopa/Levodopa | Low, 37.5/150 |

dose (mg/d) | Medium, 75/300 |

High, 150/600 | |

Dose titrate from 12.5/50 | |

Treatment duration | 40 weeks |

Washout period | A step down 3 day washout followed by 11 days off treatment |

Observations | Total of two observations by primary rater |

Before treatment (0 week) and after withdrawal (42 week) | |

Total of 12 observations by site treating investigator | |

Before treatment (screening | |

Pre-dose | |

Post-dose | |

Assumption | Rate of total UPDRS increase = 9.5 units over the 42 weeks |

10% dropout rate and >95% compliance | |

Aim | Detect a four unit difference in total UPDRS between placebo and high dose arms |

The ELLDOPA trial design assumed that all the symptomatic benefit of levodopa would be washed out by 14 days after withdrawal of levodopa treatment. This assumption was required in order to test hypotheses about the existence of a protective or a toxic effect of levodopa on disease progression.

### Input–output Models

#### Pharmacokinetic Model

A 2-compartment first-order absorption pharmacokinetic model was used to predict concentrations of levodopa after each dose. The central compartment concentration prediction (C_{1}) was used to drive the effect of levodopa on the rate of disease progression (protective or toxic).

Parameter Estimates For Simulation

Model | Parameter | Mean | PPV (%) | ||
---|---|---|---|---|---|

BSV (%) | WTV (%) | BTV (%) | |||

Pharmacokinetic | V | 11.4 | 12 | 16 | 40 |

CL (L/h/70kg) | 30.9 | 13 | 13 | 17 | |

V | 27.3 | 15 | 8 | 21 | |

CL | 34.6 | 28 | 18 | 34 | |

Natural Disease Progression | S0 (u) | 21.4 | 50 | ||

α (u/y) | 12 | 63 | |||

Symptomatic Drug Effect | BEML (u) | −20 | 75 | ||

ED50 (u/0.3g/d) | 0.0376 | 63 | |||

TEQL (days) | 642 | 149 | |||

TEML (days) | 215 | 91 | |||

Protective Drug Effect | KLD | −0.894 | 78 | ||

Residuals Error | (u) | 5.79 | – | ||

Concentration (Ce) | TEQWO (h) | 2.54 | 26 | ||

Washout Process | |||||

Direct Effect Washout Process | TEQWO (h) | 5.65 | 69 |

^{−1}was assumed for the first-order absorption rate constant. Weight is a factor in explaining the differences in the levodopa pharmacokinetic parameters between subjects (20). An allometric model was applied to standardize the pharmacokinetic parameters with an assumption of a standard body weight (

*WT*) of 70 kg (26) (Eq. 1).

#### Disease Progress Model

#### Pharmacodynamic Model

##### Symptomatic Effect for Levodopa

_{O}is the drug effect as an offset to the disease progress model.

*Ce*is the effect compartment concentration of levodopa describing the slow onset of levodopa effect. An effect compartment describes the equilibration delay between plasma concentration and the drug effect using the equilibration half-life of the effect compartment,

*TEQL*(27).

*E*max is defined as the maximum symptomatic change of total UPDRS that can be produced by levodopa. ED50 is the value of

*Ce*, relative to a 300 mg/d dose rate (median levodopa dose rate in the DATATOP study), producing 50% of

*E*max. Based on the DATATOP cohort analysis (13) and the results from an analysis of levodopa induced changes in tapping rate over 4 years (28), the time course of Emax of levodopa was described by an exponential increase approaching an asymptote,

*BEML*, with a half-life of

*TEML*(Eq. 4). Emax0 denotes the maximum effect of levodopa at time 0. However in the current study design, no treatment effect would be seen at time 0 because measurement was taken prior to dosing and levodopa concentration was 0.

*Ce*. No concentration measurements were made and

*Ce*was therefore predicted by assuming plasma concentration was proportional to the daily levodopa dose. In the ELLDOPA trial simulation,

*Ce*was predicted from the levodopa concentration and TEQL (Eq. 5).

*C*

_{1}is the levodopa concentration predicted in the central compartment of a two-compartment pharmacokinetic model.

##### Process for Washout of Symptomatic Effect

Two processes of washout of symptomatic effect were examined: effect site concentration (*Ce*) washout; and direct effect washout. The details of the direct effect washout process can be found in Appendix.

##### Effect Site Concentration Washout Process

*Ce*) after withdrawal of levodopa was modeled by an exponential decay with a half-life of TEQWO from an estimated baseline (

*Ce*

_{0}) (Eq. 6).

*E*max was computed using Eq. (4) with BEML and TEML fixed to the parameter estimates obtained from the DATATOP cohort analysis (13) and a time of 14 months (duration of levodopa treatment in the Hauser and Holford study (29)). EC50 and covariance between parameters were also fixed to the values predicted from the DATATOP cohort analysis (13). For patients taking only levodopa, the estimated TEQWO was 2.54 days ± 26% (45% of the Hauser and Holford estimated washout half life (5.65 days ± 69%)).

The concentration in the slow washout compartment (Ce_{Slow}) was predicted by continuing the solution of Eq. (5) but with *TEQL* replaced by *TEQWO*. The symptomatic effect arising from the slow washout process (SWO) was determined by the Emax model (Eq. 3) using Ce_{Slow}.

##### Functional Protective and Toxic Effects of Levodopa

*KLD*is a parameter describing the effect of levodopa on the rate of disease progression in relation to predicted plasma levodopa concentration (

*C*

_{1}).

*E*

_{s}is the drug effect on the slope of the disease progress model.

The protective effect of levodopa was modeled by the parameter *KLD*_{P}. The value of *KLD*_{P} was obtained from the DATATOP cohort analysis (13) (Table II).

A toxic effect parameter (*KLD*_{T}) was used for an adverse effect of treatment. *KLD*_{T} was assumed to produce a 2 unit worsening in total UPDRS over 9.5 months on a daily levodopa dose of 300 mg (0.4723 1/y/mg/L). This is equivalent to a 20% worsening of the rate of progression in the medium dose arm.

##### Size of Treatment Effect

With an assumption of a slow washout process of levodopa symptomatic effect, the size of treatment effect after 2 weeks levodopa withdrawal was expressed in two ways: change from baseline and difference from placebo approaches. No baseline adjustment in the simulated total UPDRS was needed in both approaches. For the change from baseline approach, baseline effect was cancelled out when computing the size of symptomatic effect (Eq. 8). For the difference from placebo approach, size of treatment effect was computed using values simulated under different assumptions of drug actions within an individual, again baseline effect was cancelled out.

##### Change from Baseline Approach

*Tlastobs*) in relation to the size of symptomatic effect at the time of the last levodopa dose (

*Tlastdose*) given on the third day of the withdrawal period (Eq. 8). This approach describes the rate of washout of symptomatic effect given the non-linear pharmacokinetic and pharmacodynamic relationship, i.e.,

*E*max model. The size of protective effect remains unchanged as the model assumed only the symptomatic effect could be washed out.

##### Difference from Placebo Approach

*Symp*

_{DFP}) and protective (

*Prot*

_{DFP}) effects at the end of the 2 weeks withdrawal period for each of the dose arms in relation to the natural disease progression in the placebo arm,

*Dprog*

_{PCB}(Eq. 9).

*Symp_SWO*

_{DOSE}is the total UPDRS in a specific dose arm simulated by a model assumed that levodopa only has symptomatic benefit and with a slow washout process.

*Prot_Symp_SWO*

_{DOSE}is the total UPDRS in a specific dose arm simulated by a model assumed that levodopa has both functional protective and symptomatic benefits and with a slow washout process for the symptomatic benefit after levodopa withdrawal. The size of protective and symptomatic effects is expressed as a percentage of the total treatment effect at the time of the last observation.

The difference from placebo approach describes the difference in total UPDRS of the treatment arms from placebo that is due to the remaining drug effects that have not been washed out. This approach differs from the change from baseline approach as it allows the prediction of the true protective effect that existed at the time of the last observation without the assumption of a continuing protective effect which is assumed not to wash out in the change from baseline approach.

##### Clinical Pharmacology Model

#### Random Effects Model

##### Between Subject Variability

*ηBSV*) was assumed to be random variable with mean zero and standard deviation of BSV. For parameters which must have the same sign for all individuals, ηBSV was assumed to arise from a lognormal distribution (Eq. 11). S0

_{ijk}is the predicted individual

*S*0 for the

*i*th subject at the

*j*th time point of the

*k*th trial and S0

_{POP}is the population value for S0. i takes in the value of 1–360 representing 360 subjects in a particular trial and k takes in the value of 1–100 representing 100 replicates.

A variance-covariance matrix was used to specify covariance between the parameters within a multivariate distribution block. The values of the variance-covariance matrix for the pharmacokinetic parameters (V_{1}, CL, V_{2} and CL_{ic}) were obtained from the population pharmacokinetic analysis (20). The values of the variance-covariance matrix for disease progression parameters (S0 and α), and the symptomatic effect parameters (BEML, C5L and TEML) were obtained from the DATATOP cohort analysis (13).

##### Residual Error

_{SD}.

#### Simulation Parameters

Parameter estimates from the DATATOP cohort analysis (13) were used as the true parameter values for simulation (Table II). In the DATATOP analysis, daily levodopa dose divided by the median dose 300 mg/d was used to normalize levodopa pharmacodynamic parameters. The relationship between daily dose and equivalent plasma concentration was calculated with a population CL of 741.6 L/d (20). The predicted average steady state concentration was then used to correct the DATATOP dose normalized parameters to the concentration based equivalent.

##### Covariate Distribution Model

A normal distribution with mean 78.65 kg and standard deviation 12.42 kg were used to simulate patient weights. These values were obtained from 20 previously untreated patients with Parkinson’s disease followed up to 4 years (30).

##### Simulation

The Pharsight Trial Simulator 2.1.2 (31) was used for data simulation on a Dual AMP Athlon 2000+ PC under Windows 2000. Differential equations were solved using a fifth order Runge-Kutta-Fehlberg algorithm.

The ELLDOPA study design, the population pharmacokinetic model and the DATATOP cohort analysis model were used as inputs for simulation. A 2 week lead-in phase was included to represent the screening visit where no medications were given. The study timeline for simulation was 44 weeks (2 weeks lead-in phase, 40 weeks treatment and 2 weeks washout). Subjects were assumed to be enrolled in a single center.

The ELLDOPA study design was simulated with combinations of different assumptions on the drug action model and the speed of the washout process. No missing observations were generated in the simulation step. A scenario is defined as a simulated trial with specified assumptions on the study design and drug model. Three main scenarios were simulated with different assumptions on the drug action model.

##### Scenario I

##### Scenario II

The second assumption was that levodopa has symptomatic benefits as well as a functional protective effect. This assumption was based on the findings of the DATATOP cohort analysis (13). In this case, KLD in Eq. (7) was in fact KLD_{P} and its parameter estimate was obtained from the DATATOP cohort analysis (13) (Table II).

##### Scenario III

The third assumption was that levodopa has symptomatic benefits and a functional toxic effect. In this case, KLD in Eq. (7) was changed to KLD_{T} and its value was computed by assuming an equivalent to a 20% worsening of the rate of progression in the medium dose arm (see Functional Protective and Toxic Effect of Levodopa section).

##### Model Validation

The simulation model was qualified by comparing the Trial Simulator total UPDRS with values simulated using NONMEM (32) without random sources of variability.

#### Bootstrapping For Confidence Interval

Bootstrapping approaches were used to assess the imprecision of the estimated size of treatment effects of levodopa. As 100 replications were simulated for each scenario, 100 parameters (decision or statistic) resulted. A bootstrap sample was generated by repeated random sampling, with replacement from the set of 100 interested parameters. The 95% confidence interval was computed from 1,000 bootstrap samples for each of the scenarios.

## RESULTS

### Treatment Effect of Levodopa

Predicted Differences of Change From Baseline in Total UPDRS at 42 Weeks

Drug Action | Washout | Effect Site Concentration Washout Process | ||||||||
---|---|---|---|---|---|---|---|---|---|---|

Low | Medium | High | ||||||||

Mean | 95% CI | Mean | 95% CI | Mean | 95% CI | |||||

Symptomatic + Protective | Fast | 2.0 | 1.7 | 2.2 | 3.0 | 2.8 | 3.3 | 4.2 | 3.9 | 4.4 |

Slow | 3.8 | 3.5 | 4.1 | 5.9 | 5.7 | 6.2 | 8.4 | 8.1 | 8.7 | |

Symptomatic | Fast | −0.1 | −0.4 | 0.2 | 0.2 | −0.1 | 0.5 | 0.2 | 0.04 | 0.5 |

Slow | 1.9 | 1.6 | 2.2 | 2.7 | 2.4 | 3.0 | 4.1 | 3.8 | 4.4 | |

Symptomatic + Toxic | Fast | −1.2 | −15 | −1.0 | −2.4 | −2.7 | −2.2 | −5.8 | −6.1 | −5.5 |

Slow | 1.0 | 0.7 | 1.2 | 0.4 | 0.1 | 0.7 | −1.4 | −1.7 | −1.1 |

### Size of Treatment Effect

#### Change from Baseline Approach

Size of Treatment Effects After 2 Weeks Levodopa Withdrawal Under An Assumption of Slow Washout of Symptomatic Benefits

Washout Process | Size of Effect (%) | Change from Baseline | Difference from Placebo | ||||||||||||||||
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|

Low | Medium | High | Low | Medium | High | ||||||||||||||

Mean | 95% CI | Mean | 95% CI | Mean | 95% CI | Mean | 95% CI | Mean | 95% CI | Mean | 95% CI | ||||||||

Effect Site Concentration | Symptomatic | 21.1 | 21.0 | 21.3 | 27.4 | 27.3 | 27.6 | 37.3 | 37.0 | 37.6 | 59.2 | 54.0 | 66.6 | 57.2 | 52.1 | 66.1 | 57.1 | 53.9 | 61.5 |

Protective | – | – | – | – | – | – | – | – | – | 39.4 | 32.0 | 44.7 | 40.8 | 31.7 | 45.9 | 40.7 | 36.2 | 43.8 |

#### Difference from Placebo Approach

With the assumption that levodopa has both symptomatic and functional protective benefits, the Ce washout process expected a relatively constant fraction of symptomatic (57–59%) and protective (39–41%) drug effects after 2 weeks levodopa withdrawal (Table IV). In short, up to 41% of the predicted difference in total UPDRS between the levodopa and placebo treated groups after 2 weeks levodopa withdrawal was accounted by the protective effect.

## DISCUSSION

### Clinical Pharmacology Model

#### Time Course of Symptomatic Response

The time needed to wash out the symptomatic component of levodopa effects on total UPDRS is critical to the interpretation of the results of the ELLDOPA study. There are reasons to question the completeness of washout of symptomatic effects by two weeks as done in the ELLDOPA trial. The full therapeutic benefits of levodopa have a slow onset of action. Using the total UPDRS to assess disease severity, a long onset time with a half-life of 642 days was estimated in the DATATOP cohort analysis (13). It is usually assumed that the time course of onset is mirrored in the time course of offset of response. However, Hauser and Holford (29) reported that the loss of clinical benefit following withdrawal of levodopa has an estimated half-life of 5.65 days. There may be several phases to the loss of effect just as different phases have been recognized for the delay in onset. In addition to the quantitative description of washout of response a panel of movement disorder experts was asked to decide if full washout had occurred by 15 days after stopping treatment. By examining the plots of total UPDRS scores *versus* days after withdrawal they found only 23% of patients appeared to have fully washed out. These observations imply that a 2 week washout would not be long enough to separate levodopa effects on rate of disease progression from its symptomatic benefits.

Our model predicts that 21–37% (Ce washout process) of symptomatic effect remained at 2 weeks after low and high dose levodopa withdrawal (Table IV). The predicted size of symptomatic effect with the Ce washout process is higher but comparable with the time course simulated using the estimated washout half-life of 5.65 days from Hauser and Holford, (29) i.e. 18% remained after 14 days of washout (Fig. 2).

### Size of Treatment Effect

In our simulation models, only symptomatic effect is allowed to be washed out. The implementation of the direct effect washout process in our analysis is different from that used by Hauser and Holford (29) because they could not distinguish between washout of symptomatic and protective components. However, using a simulation model we could use a direct effect slow washout process and apply it to the symptomatic component alone. The protective effect of levodopa on the rate of progression is assumed to be lost immediately after drug withdrawal but the benefits of slowed progression from previous treatment persist. While it is reasonable to propose that the protective effect on rate of progression might be washed out slowly it would be very hard in practice to distinguish immediate from slow protective effect washout. The difference is shown in Fig. 4 by comparing the slow symptomatic washout time courses with and without protective effect and immediate compared with slow protective washout. We predict that 50 ± 14% (direct effect washout process) of the observed difference between the high dose levodopa group and placebo is attributable to slowing of disease progression (Table IV). It should be noted that because of stochastic influences on the simulation parameters, the sum of the protective and symptomatic effects does not necessarily sum to 100%.

### Protective or Toxic Effects on Disease Progression?

Assuming levodopa had a dose-responsive toxic effect as well as a beneficial symptomatic effect, an improvement in total UPDRS was predicted for the low and medium dose arms at 42 weeks by the symptomatic and toxic model with slow washout process (Table III) one might falsely conclude that levodopa has a protective rather than toxic effect. However at the highest dose the underlying toxic effect was revealed by worsening of total UPDRS at 42 weeks.

#### Implications for Interpretation of ELLDOPA Results

Observed and Predicted Total UPDRS Change from Baseline For Placebo Treatment at 42 Weeks and Difference from Placebo For Levodopa Treatment Arms

Effect of levodopa on disease progression | Scenario | Placebo | Low | Medium | High |
---|---|---|---|---|---|

Observed ELLDOPA | 7.8 ± 1.1 | 5.9 ± 0.7 | 5.9 ± 0.8 | 9.2 ± 0.9 | |

Protective | Predicted Effect Site Concentration Washout | 9.9 ± 1.0 | 3.8 ± 1.4 | 5.9 ± 1.3 | 8.4 ± 1.3 |

Toxic | Predicted Effect Site Concentration Washout | 10.0 ± 1.0 | 1.0 ± 1.3 | 0.4 ± 1.5 | −1.4 ± 1.5 |

The observed difference from placebo could arise from an initial washout of symptomatic effect (as observed in ELLDOPA) with a subsequent washout (not observed because of the short period of withdrawal) of all effects without any protective effect. However, there is no direct evidence to support the hypothesis that all levodopa effects will eventually be washed out. In contrast the close quantitative prediction of the observed effects based on the DATATOP cohort (13) and the study by Hauser *et al* (33) provide strong support for the observed ELLDOPA results being due to a combination of continuing protective effect and partial washout of symptomatic effect.

Simply observing the change from baseline is not adequate for distinguishing between symptomatic and protective effects in degenerative diseases (1) unless the washout period is sufficient to completely eliminate any symptomatic effect. We predict the time necessary for complete withdrawal of effects to be in excess of 25 days with a washout half-life of 5.65 days. Therefore the ELLDOPA study is incapable of identifying the effect of levodopa on natural disease progression from symptomatic benefits using the change from baseline method of analysis.

Our simulation successfully predicted the magnitude of change in placebo and treated groups (Tables V and VIII) and gives us some confidence that we can also predict that between 40 to 50% of the total UPDRS difference from placebo is due solely to protective effects (Tables IV and VII and Fig. 4).

In summary, the current analysis provided an external validation of the predictions of the DATATOP cohort analysis. Our analysis of the ELLDOPA trial results confirm the prediction from our model based on the DATATOP that levodopa has functional protective effects and finds no evidence for a toxic effect on disease progression. The observed dose response relationship is exactly opposite to what would be predicted if levodopa accelerated the rate of progression. These findings indicate that there is no reason to delay levodopa because of possible toxic effects and that there may be benefits to beginning symptomatic therapy earlier rather than later. Symptomatic therapy could include dopamine agonists as well as levodopa and deprenyl as suggested by our results of modeling the DATATOP cohort.