Pharmacopsychiatry 2009; 42: S129-S143
DOI: 10.1055/s-0029-1202848
Original Paper

© Georg Thieme Verlag KG Stuttgart · New York

Intermittent Adaptation

A Theory of Drug Tolerance, Dependence and AddictionA. Peper 1
  • 1Department of Medical Physics, Academic Medical Centre, University of Amsterdam, Amsterdam, The Netherlands
Weitere Informationen

Publikationsverlauf

Publikationsdatum:
11. Mai 2009 (online)

Abstract

A mathematical model of drug tolerance and its underlying theory is presented. The model is essentially more complex than the generally used model of homeostasis, which is demonstrated to fail in describing tolerance development to repeated drug administrations. The model assumes the development of tolerance to a repeatedly administered drug to be the result of a regulated adaptive process. The oral detection and analysis of endogenous substances is proposed to be the primary stimulus for the mechanism of drug tolerance. Anticipation and environmental cues are in the model considered secondary stimuli, becoming primary only in dependence and addiction or when the drug administration bypasses the natural – oral – route, as is the case when drugs are administered intravenously. The model considers adaptation to the effect of a drug and adaptation to the interval between drug taking autonomous tolerance processes. Simulations with the mathematical model demonstrate the model's behaviour to be consistent with important characteristics of the development of tolerance to repeatedly administered drugs: the gradual decrease in drug effect when tolerance develops, the high sensitivity to small changes in drug dose, the rebound phenomenon and the large reactions following withdrawal in dependence. Simulations of different ways withdrawal can be accomplished, demonstrates the practical applicability of the model.

References

  • 1 Ahmed SH, Kenny PJ, Koob GF. et al . Neurobiological evidence for hedonic allostasis associated with escalating cocaine use.  Nat Neurosci. 2002;  5 ((7)) 625-626
  • 2 Ahmed SH, Koob GF. Transition to drug addiction: a negative reinforcement model based on an allostatic decrease in reward function.  Psychopharmacology. 2005;  180 ((3)) 473-490
  • 3 Alexander R McN. Symmorphosis and safety factors. In: Principles of Animal Design. The Optimization and Symmorphosis Debate. Weibel ER, Taylor CR, Bolis L, eds. Cambridge University Press: Cambridge 1998: 28-35
  • 4 Bacigalupe LD, Bozinovic F. Design, limitations and sustained metabolic rate: lessons from small mammals.  J Exp Biol. 2002;  205 2963-2970
  • 5 Bell D, Griffin AWJ. (ed). Modern Control Theory and computing. McGraw-Hill: London 1969
  • 6 Bennett AF. Structural and functional determinates of metabolic rate.  Amer Zool. 1988;  28 699-708
  • 7 Bernard C. Leçons sur les Phénomènes de la Vie Communs aux Animaux et aux Végétaux. Bailliére et Fils: Paris 1878
  • 8 Bertalanffi L von. Zu einer allgemeinen Systemlehre.  Biologia Generalis. 1949;  195 114-129
  • 9 Bertalanffi L von. An outline of General Systems Theory.  Brit J Phil Science. 1950;  1 139-164
  • 10 Cannon WB. Organization for physiological homeostasis.  Physiological Reviews. 1929;  9 399-431
  • 11 Deutsch R. Conditioned hypoglycemia: a mechanism for saccharid-induced sensi-tivity to insulin in the rat.  J Comp Physiol Psychol. 1974;  86 350-358
  • 12 Diamond JM, Hammond KA. The matches, achieved by natural selection, between biological capacities and their natural loads.  Experientia. 1992;  48 51-557
  • 13 Dudley R, Gans C. A critique of symmorphosis and optimality models in physiology.  Physiol Zool. 1991;  64 627-637
  • 14 Dudley R, Huey RB, Carrier DR. Living History of Physiology: Carl Gans.  Advan Physiol Educ. 2006;  30 102-107
  • 15 Dworkin BR. Learning and Physiological Regulation. Un. of Chicago Press: Chicago 1993
  • 16 Garland Jr T, Huey RH. Testing symmorphosis: does structure match functional requirements?.  Evolution. 1987;  41 1404-1409
  • 17 Goldstein A, Goldstein DB. Enzyme expansion theory of drug tolerance and physical dependence. In: The Addictive States. Wikler A, ed. Res Publ Assoc Res Nerv Ment Dis. Williams & Wilkins: Baltimore 1968 46: 265
  • 18 Grill HJ, Berridge KC, Ganster DJ. Oral glucose is the prime elicitor of preabsorptive insulin secretion.  Am J Physiol. 1984;  246 R88-R95
  • 19 Harrison JF, Camazine S, Marden JH. et al . Mite not make it home: tracheal mites reduce the safety margin for oxygen delivery of flying honeybees.  J Exp Biol. 2001;  204 ((4)) 805-814
  • 20 Heding LG, Munkgaard Rasmussen S. Human C-peptide in normal and diabetic subjects.  Diabetol. 1975;  11 201-206
  • 21 Jaffe JH, Sharpless SK. Pharmacological denervation super sensitivity in the central nervous system: A theory of physical dependence. In: The Addictive States. Wikler A (ed). Res Publ Assoc Res Nerv Ment Dis. Williams & Wilkins: Baltimore 1968 46: 226
  • 22 Johnson S, Faull C. The absence of “cross-tolerance” when swithcing from oral morphine to transdermal fenraniel.  Palliat Med. 1997;  11 494-495
  • 23 Kalant H, LeBlanc AE, Gibbins RJ. Tolerance to, and dependence on, some non-opiate psychotropic drugs.  Pharmacol Revsnyder. 1971;  23 ((3)) 135-191
  • 24 Kandel ER. Cellular basis of behavior; an introduction to behavioral neurobiology. Freeman and Comp: San Fransisco 1976
  • 25 Koob GF, Le Moal M. Drug addiction, dysregulation of reward, and allostasis.  Neuropsychopharmacology. 2001;  24 97-129
  • 26 Koob GF, Le Moal M. Plasticity of reward neurocircuitry and the “dark side” of drug addiction.  Nat Neurosci. 2005;  8 1442-1444
  • 27 Koshland DE. A response regulator model in a simple sensory system.  Science. 1977;  196 1055-1063
  • 28 Loewy AD, Haxhiu MA. CNS cell groups projecting to pancreatic parasympathetic preganglionic neurons.  Brain Res. 1993;  620 323-330
  • 29 Martin WR. A homeostatic and redundancy theory of tolerance to and dependence on narcotic analgesics. In: The Addictive States. Wikler A (ed) Res Publ Assoc Res Nerv Ment Dis. Williams & Wilkins: Baltimore 1968 46: 206
  • 30 Mirel RD, Ginsberg-Fellner F, Horwitz DL. et al . C-peptide reserve in insulin-dependent diabetes: Comparative responses to glucose, glucagon and tabutamide.  Diabetol. 1980;  19 183-188
  • 31 Mitchell D, Snellen JW, Atkins AR. Thermoregulation during Fever: Change of Set-Point or Change of Gain.  Pflugens Arch. 1970;  321 393
  • 32 Peper A, Grimbergen CA, Kraal JW. et al . An approach to the modelling of the tolerance mechanism in the drug effect. Part I: The drug effect as a disturbance of regulations.  J Theor Biol. 1987;  127 413-426
  • 33 Peper A, Grimbergen CA, Kraal JW. et al . An approach to the modelling of the tolerance mechanism in the drug effect. Part II: On the implications of compensatory regulations.  J Theor Biol. 1988;  132 29-41
  • 34 Peper A, Grimbergen CA, Spaan JAE. et al . A mathematical model of the hsp70 regulation in the cell.  Int J Hyperthermia. 1998;  14 ((1)) 97-124
  • 35 Peper A, Grimbergen CA. Preliminary results of simulations with an improved mathematical model of drug tolerance.  J Theor Biol. 1999;  199 119-123
  • 36 Peper A. A theory of drug tolerance and dependence I: a conceptual analysis.  J Theor Biol. 2004a;  229 477-490
  • 37 Peper A. A theory of drug tolerance and dependence II: the mathematical model.  J Theor Biol. 2004b;  229 491-500
  • 38 Perry PJ, Alexander B. Sedative/hypnotic dependence: patient stabilization, tolerance testing and withdrawal.  Drug Intell Clin Pharm. 1986;  20 532-537
  • 39 Poulos CX, Cappell H. Homeostatic theory of drug tolerance: A general model of physiological adaptation.  Psychological Review. 1991;  98 390-408
  • 40 Rescorla RA, Wager AR. A theory of Pavlovian conditioning: Variations in the effectiveness of reinforcement and non-reinforcement. In: Classical conditioning II: Current research and theory. Black AH, Prokasy WF (eds) Appleton-Century-Crofts: New York 1972 64–99
  • 41 Rickels K, Schweizer E, Weiss S. Maintenance drug treatment for panic disorder: short- and long-term outcome after drug taper.  Arch Gen Psychiatry. 1993;  50 61-68
  • 42 Rickels K, Schweizer E, Garcia Espana F. et al . Trazodone and valproate in patients discontinuing long-term benzodiazepine therapy: effects on withdrawal symptoms and taper outcome.  Psychopharmacology (Berl). 1999;  141 ((1)) 1-5
  • 43 Ricklefs RE. The concept of symmorphosis applied to growing birds. In: Principles of Animal Design. The Optimization and Symmorphosis Debate. Weibel ER, Taylor CR and Bolis L (eds). Cambridge University Press: Cambridge 1998: 56-62
  • 44 Saunders PT, Koeslag JH, Wessels JA. Integral Rein Control in Physiology.  J Theor Biol. 1998;  194 163-173
  • 45 Schulkin J. Rethinking homeostasis: allostatic regulation in physiology and pathophysiology. MIT Press: Cambridge, Mass 2003
  • 46 Schweizer E, Rickels K, De Martinis N. et al . The effect of personality on withdrawal severity and taper outcome in benzodiazepine dependent patients.  Psychol Med. 1998;  28 ((3)) 713-720
  • 47 Siegel S. Evidence from rats that morphine tolerance is a learned response.  J Comp Physiol Psychol. 1975;  89 498-506
  • 48 Siegel S, Hinson RE, Krank MD. et al . Heroin “Overdose” death: Contribution of drug-associated environmental cues.  Science. 1982;  216 436-437
  • 49 Siegel S. Learning and homeostasis.  Integr Phys Behav Science. 1996;  31 ((2)) 189
  • 50 Siegel S, Allan LG. Learning and homeostasis: Drug addiction and the McCollough effect.  Psychol Bull. 1998;  124 ((2)) 230-239
  • 51 Siegel S. Drug anticipation and drug addiction. The 1998 H. David Archibald lecture.  Addiction. 1999;  94 ((8)) 1113-1124
  • 52 Snyder SH. Opiate receptors and internal opiates.  Sci Am. 1977;  236 44-56
  • 53 Solomon RL, Corbit JD. An opponent-process theory of motivation. II: cigarette addiction.  J Abnorm Psychol. 1973;  81 158-171
  • 54 Solomon RL, Corbit JD. An opponent-process theory of motivation. I: temporal dynamics of affect.  Psychol Rev. 1974;  81 119-145
  • 55 Solomon RL. An opponent-process theory of acquired motivation: The affective dynamics of addiction. In: Psychopathology: experimental models. Maser JD, Seligman MEP (eds) Freeman: San Francisco 1977: 66-103
  • 56 Solomon RL. The opponent-process theory of acquired motivation: The costs of pleasure and the benefits of pain.  Am Psychol. 1980;  35 691-712
  • 57 Steffens AB. Influence of the oral cavity on insulin release in the rat.  Am J Physiol. 1976;  230 1411-1415
  • 58 Sterling P, Eyer J. Allostasis: a new paradigm to explain arousal pathology. In: Handbook of Life Stress, Cogintion and Health. Fisher S, Reason J, (eds) Wiley & Sons: New York 1988 629–649
  • 59 Sterling P. Principles of allostasis: optimal design, predictive regulation, pathophysiology and rational therapeutics. In: Allostasis Homeostasis and the Costs of Adaptation. Schulkin J (ed). Cambridge University Press: Cambridge, England 2004
  • 60 Taylor CR, Weibel ER. Design of the mammalian respiratory system. I. Problem and strategy.  Respir Physiol. 1981;  44 1-10
  • 61 Thorpe WH. Learning and instinct in animals. Methuen and Co: London 1956
  • 62 Tillil H, Shapiro ET, Miller MA. et al . Dose-dependent effects of oral and intravenous glucose on insulin secretion and clearance in normal humans.  Am J Physiol. 1988;  254 E349-E357
  • 63 Toates FM. Homeostasis and drinking.  Beh Brain Sciences. 1979;  2 95-136
  • 64 Verveen AA. Silent endocrine tumors. A steady-state analysis of the effects of changes in cell number for biological feedback systems.  Biol Cybern. 1978;  31 49
  • 65 Verveen AA. Theory of diseases of steady-state proportional control systems.  Biol Cybern. 1983;  47 25
  • 66 Wiegant FAC, Spieker N, van Wijk R. Stressor-specific enhancement of hsp induction by low doses of stressors in conditions of self- and cross-sensitization.  Toxicology. 1998;  127 ((1-3)) 107-119
  • 67 Wiener N. Cybernetics: or control and communication in the animal and the machine. John Wiley: New York 1948

Appendix

In a previous paper, the mathematical implementation of the model and the derivation of the formulae describing its components is extensively discussed [37]. In this appendix, a summary is given of the formulae. For the sake of brevity, the index ‘(t)’ in time signals is omitted. [Fig. A1] shows a block diagram of the mathematical model.

Zoom Image

Fig. A1 Block diagram of the mathematical model.

1. The digestive tract

The digestive system plays no role in the regulation loop. Drug transport through the digestive tract is modelled as a first order function:

Zoom Image

The input to the block is the drug administration, drug. The input signal is integrated to obtain the drug level when it enters the bloodstream, the output of the block Sdigest. A fraction 1/Tdigest of the output signal is subtracted from the input to account for the spread in drug distr ibution in the diges tive tract. Tdigest is the time constant of this process.

2. The bloodstream

After digestion, the drug enters the bloodstream where it is dispersed. In the present configuration of the model, the drug and the substance produced by the process are assumed to be identical in composition and consequently add in the bloodstream. The amount of the total substance in the bloodstream will be reduced by the body's metabolism. The processes are modelled with a first order function:

Zoom Image

The input signals – the drug as it moves from the digestive tract into the bloodstream, Sdigest, and the substance produced by the process, Sprocess – are added and integrated, yielding the output of the block, the blood drug level Sblood. To account for the body's metabolism, a fraction 1/Tblood of the output signal is subtracted from the input.

3. The adaptive regulator

The input signals of the adaptive regulator are the drug administration and the sensor signal, processed by the loop control block. The sensor signal provides the information about the drug effect. The adaptive regulator comprises a fast and a slow regulator.

3a. The fast regulator

[Fig. A2] shows a block diagram of the fast regulator. The fast regulator consists of the blocks ‘drug regulator’, ‘interval regulator’ and ‘model estimation’. [Fig. A3] shows the implementation of the fast regulator in the mathematical simulation program Simulink. The input signal of the drug regulator Sd is multiplied by Mdrug, which represents the course of the drug level in the input signal over time. This signal is integrated (1/s) with a time constant Tdrug, yielding its average. The resulting value is a slowly rising signal, Ldrug. Multiplying Ldrug by Mdrug yields the output signal Sdrug.

The relation between the signals is:

Zoom Image

Fig. A2 Block diagram of the adaptive regulator.

Zoom Image

Fig. A3 The fast regulator implemented in Simulink.

Zoom Image

And

Zoom Image

The input to the interval regulator is obtained when the output signal of the drug regulator – Sdrug – is subtracted from its top value Ldrug. The model of the interval is Mint.

The relation between the signals in the fast regulator describing the drug's presence is then:

Zoom Image

and

Zoom Image

Similarly, the equation describing the interval regulator is:

Zoom Image

and

Zoom Image

The output of the interval regulator is Sint. The output signal of the total fast regulator is obtained by subtracting the interval signal from the top level of the drug signal:

Zoom Image

3b. Estimation of the drug effect in the adaptive regulator

The model of the course of the drug concentration when it enters the bloodstream – Mdrug – is computed by calculating the effect of a pulse with a magnitude of 1 on the digestive tract's transfer function. The input of the interval is acquired when the signal “drug” is subtracted from its top value: 1. Multiplying this signal with the transfer of the digestive tract yields the model of the interval Mint:

Zoom Image

And

Zoom Image

Tdigest is the time constant of the digestive system.

3c. The slow regulator

The slow regulator counteracts the disturbance by lowering the level of the process with the average of the drug effect. This is obtained by a low pass filter with a time constant Tslow:

Zoom Image

4. The process

The model does not incorporate the characteristics of the process and the process regulator. In a specific model of drug tolerance where the process is included, the effect of the process transfer on loop stability has to be controlled by the “The loop control” block.

5. Loop control

A loop control provides the necessary conditions for stable operation of the negative feedback system. In the present form of the model, the effect of the bloodstream on the regulation loop is counteracted. The relation between the input and the output of the loop control is:

Zoom Image

6. The sensor

The sensor transforms the chemical signal Sblood – the blood drug level – into the signal Ssense. This transformation is in the present model assumed to be linear and is set at 1. In specific models of physiological processes, this complex mechanism can be described more accurately. Stable operation then requires that the effect of its transfer on loop stability is controlled by the “The loop control” block.

Disclosure

The author declares that there are no financial interests to disclose.

Correspondence

A. PeperPhD 

Milletstraat 48-3

1077 Zg Amsterdam

The Netherlands

Telefon: +31/20 675 10 00

eMail: a.peper@planet.nl

URL: http://www.abrahampeper.com